THE HANDBOOK OF ROAD SAFETY MEASURES SECOND EDITION
RELATED BOOKS HIMANEN, LEE-GOSSELIN & PERRELS – Building Blocks for Sustainable Transport: Obstacles, Trends, Solutions DAVID SHINAR – Traffic Safety and Human Behaviour STOPHER & STECHER – Travel Survey Methods Quality and Future Directions HENSHER & BUTTON (eds.) – Handbooks in Transport FULLER & SANTOS – Human Factors for Highway Engineers GAUDRY & LASSARE (eds.) – Structural Road Accident Models DAGANZO – Fundamentals of Transportation and Traffic Operations
THE HANDBOOK OF ROAD SAFETY MEASURES SECOND EDITION
BY
Rune Elvik Institute of Transport Economics, Oslo, Norway
Alena Høye Institute of Transport Economics, Oslo, Norway
Truls Vaa Institute of Transport Economics, Oslo, Norway
Michael Sørensen Institute of Transport Economics, Oslo, Norway
United Kingdom – North America – Japan India – Malaysia – China
Emerald Group Publishing Limited Howard House, Wagon Lane, Bingley BD16 1WA, UK First edition 2004 Second edition 2009 Copyright r 2009 Emerald Group Publishing Limited Reprints and permission service Contact:
[email protected] No part of this book may be reproduced, stored in a retrieval system, transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without either the prior written permission of the publisher or a licence permitting restricted copying issued in the UK by The Copyright Licensing Agency and in the USA by The Copyright Clearance Center. No responsibility is accepted for the accuracy of nformation contained in the text, illustrations or advertisements. The opinions expressed in these chapters are not necessarily those of the Editor or the publisher. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN: 978-1-84855-250-0
Awarded in recognition of Emerald’s production department’s adherence to quality systems and processes when preparing scholarly journals for print
v
T ABLE
OF
C ONTENTS
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
xi
PART I Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
1. Background and Guide to Readers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3
1.1. 1.2. 1.3. 1.4.
Purpose of the Handbook of Road Safety Measures . . . . . . . . . . . . . . . . . . . Which questions does the book answer? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Structure of the book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Science and politics in road safety. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3 5 6 8
2. Literature Survey and Meta-Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
15
2.1. 2.2. 2.3. 2.4. 2.5. 2.6. 2.7. 2.8.
Systematic literature search. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Criteria for study inclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Study classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The use of meta-analysis to summarise study results . . . . . . . . . . . . . . . . . . Does a weighted mean estimate of effect make sense? . . . . . . . . . . . . . . . . . Developing accident modification functions . . . . . . . . . . . . . . . . . . . . . . . . . . Specification of accident or injury severity . . . . . . . . . . . . . . . . . . . . . . . . . . . Updated estimates of effect: Revision of the book . . . . . . . . . . . . . . . . . . . .
15 19 19 20 25 30 32 33
3. Factors Contributing to Road Accidents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
35
3.1. 3.2. 3.3. 3.4. 3.5. 3.6. 3.7. 3.8. 3.9.
A simple conceptual framework. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The scope of the road accident problem worldwide . . . . . . . . . . . . . . . . . . . Incomplete reporting in official road accident statistics. . . . . . . . . . . . . . . . Exposure: Traffic volume. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Accident rates for different types of exposure. . . . . . . . . . . . . . . . . . . . . . . . . The mixture of road users . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A survey of some risk factors for accident involvement. . . . . . . . . . . . . . . . A survey of risk factors for injury severity . . . . . . . . . . . . . . . . . . . . . . . . . . . Assessing the relative importance of risk factors. . . . . . . . . . . . . . . . . . . . . .
35 37 47 53 56 57 59 67 69
vi
4. Basic Concepts of Road Safety Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. 4.2. 4.3. 4.4.
81
Random and systematic variation in accident counts. . . . . . . . . . . . . . . . . . The use of accident rates to measure safety. . . . . . . . . . . . . . . . . . . . . . . . . . . Explaining road accidents – the concept of cause . . . . . . . . . . . . . . . . . . . . . Road accidents as a self-regulatory problem. . . . . . . . . . . . . . . . . . . . . . . . . .
81 86 87 93
5. Assessing the Quality of Evaluation Studies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
99
5.1. 5.2. 5.3. 5.4. 5.5.
The concept of study quality. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 Assessing study quality. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 The importance of study quality: Some illustrations. . . . . . . . . . . . . . . . . . . 106 The treatment of study quality in meta-analysis . . . . . . . . . . . . . . . . . . . . . . 113 Can the findings of road safety evaluation studies be accounted for in theoretical terms?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
6. The Contribution of Research to Road Safety Policy-Making. . . . . . . . . . . . . . . . 6.1. 6.2. 6.3. 6.4.
117
An idealised model of the policy-making process . . . . . . . . . . . . . . . . . . . . . The applicability of cost–benefit analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . Monetary valuation of road safety in different countries. . . . . . . . . . . . . . . Current monetary valuations of impacts of road safety measures in Norway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5. The preventability of road accident fatalities and injuries. . . . . . . . . . . . . . 6.6. Vision Zero . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
125 127 130 131
PART II Road Safety Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
143
1. Road Design and Road Equipment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
145
1.0. 1.1. 1.2. 1.3. 1.4. 1.5. 1.6. 1.7. 1.8. 1.9. 1.10. 1.11. 1.12.
Introduction and overview of 20 measures . . . . . . . . . . . . . . . . . . . . . . . . . . . Cycle lanes and tracks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Motorways. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bypasses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Urban arterial roads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Channelisation of junctions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Roundabouts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Redesigning junctions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Staggered junctions (reconfiguring crossroads to two T-junctions). . . . . . Grade-separated junctions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Black spot treatment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-section improvements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Roadside safety treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
117 119 124
145 155 164 169 172 178 185 190 195 199 206 212 230
vii
1.13. 1.14. 1.15. 1.16. 1.17. 1.18. 1.19. 1.20.
Improving road alignment and sight distance. . . . . . . . . . . . . . . . . . . . . . . . . Reconstruction and rehabilitation of roads. . . . . . . . . . . . . . . . . . . . . . . . . . . Guardrails and crash cushions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Game accident measures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Horizontal curve treatments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Road lighting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Improving tunnel safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rest stops and service areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
233 248 251 258 268 272 281 287 289
2. Road Maintenance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
335
2.0. 2.1. 2.2. 2.3. 2.4. 2.5. 2.6. 2.7.
Introduction and overview of nine measures. . . . . . . . . . . . . . . . . . . . . . . . . . Resurfacing of roads. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Treatment of unevenness and rut depth of the road surface. . . . . . . . . . . . Improving road surface friction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bright road surfaces. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Landslide protection measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Winter maintenance of roads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Winter maintenance of pavements, footpaths, cycle paths and other public areas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.8. Correcting erroneous traffic signs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.9. Traffic control at roadwork sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
373 376 380 385
3. Traffic Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
397
3.0. 3.1. 3.2. 3.3. 3.4. 3.5. 3.6. 3.7. 3.8. 3.9. 3.10. 3.11. 3.12. 3.13. 3.14.
Introduction and overview of 22 measures . . . . . . . . . . . . . . . . . . . . . . . . . . . Area-wide traffic calming. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Environmental streets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pedestrian streets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Urban play streets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Access control. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Priority control. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yield signs at junctions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stop signs at junctions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Traffic signal control at junctions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Signalised pedestrian crossings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Speed limits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Speed-reducing devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Road markings. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Traffic control for pedestrians. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
335 339 344 348 358 360 363
397 403 408 412 415 419 423 427 430 433 440 445 452 458 467
viii
3.15. 3.16. 3.17. 3.18. 3.19. 3.20. 3.21. 3.22.
Stopping and parking control. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . One-way streets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reversible traffic lanes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bus lanes and bus stop design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dynamic route guidance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Variable message signs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Protecting railway–highway level crossings. . . . . . . . . . . . . . . . . . . . . . . . . . . Environmental zones. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
474 479 481 487 492 495 499 504 507
4. Vehicle Design and Protective Devices. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
543
4.0. Introduction and overview of 29 measures. . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Tyre tread depth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Studded tyres . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3. Antilock braking systems and disc brakes . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4. High-mounted stop lamps. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5. Daytime running lights for cars. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6. Daytime running lights for mopeds and motorcycles. . . . . . . . . . . . . . . . . . 4.7. Improving vehicle headlights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.8. Reflective materials and protective clothing. . . . . . . . . . . . . . . . . . . . . . . . . . 4.9. Steering, suspension and vehicle stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.10. Bicycle helmets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.11. Motorcycle helmets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.12. Seat belts in cars. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.13. Child restraints. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.14. Airbags in cars. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.15. Seat belts in buses and trucks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.16. Vehicle crashworthiness. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.17. Driving controls and instruments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.18. Intelligent cruise control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.19. Regulating vehicle mass (weight). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.20. Regulating automobile engine capacity (motor power) and top speed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.21. Regulating engine capacity (motor power) of mopeds and motorcycles. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.22. Under-run guards on heavy vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.23. Safety equipment on heavy vehicles. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.24. Moped and motorcycle equipment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.25. Bicycle safety equipment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.26. Safety standards for trailers and caravans . . . . . . . . . . . . . . . . . . . . . . . . . . .
543 550 554 560 564 567 571 574 582 586 591 596 600 609 615 624 627 635 639 642 649 656 661 663 668 671 676
ix
4.27. Fire safety standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.28. Hazardous goods regulations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.29. Electronic stability control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
680 682 687 690
5. Vehicle and Garage Inspection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
733
5.0. 5.1. 5.2. 5.3. 5.4.
Introduction and overview of four measures. . . . . . . . . . . . . . . . . . . . . . . . . . Vehicle safety standards. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Periodic motor vehicle inspections. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Roadside vehicle inspections. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Garage regulation and inspections. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
733 737 742 749 753 755
6. Driver Training and Regulation of Professional Drivers . . . . . . . . . . . . . . . . . . . . .
759
6.0. Introduction and overview of 12 measures. . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1. Driving licence age limits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2. Health requirements for drivers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3. Driver performance standards. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4. Basic driver training. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5. The driving test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6. Training and testing of moped and motorcycle riders. . . . . . . . . . . . . . . . . 6.7. Training and testing of professional drivers. . . . . . . . . . . . . . . . . . . . . . . . . . 6.8. Graduated driving licences (GDLs) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.9. Motivation and incentive systems in the work place . . . . . . . . . . . . . . . . . . 6.10. Regulation of driving and rest hours. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.11. Safety standards for emergency driving. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.12. Safety standards for school transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
759 763 771 779 785 793 797 802 806 815 817 827 833 839
7. Public Education and Information. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
859
7.0. 7.1. 7.2. 7.3.
Introduction and overview of three measures. . . . . . . . . . . . . . . . . . . . . . . . . Education of pre-school children (0–6 years) . . . . . . . . . . . . . . . . . . . . . . . . . Education in schools (6–18 years old). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Road user information and campaigns. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
859 862 865 867 873
8. Police Enforcement and Sanctions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
879
8.0. Introduction and overview of 13 measures. . . . . . . . . . . . . . . . . . . . . . . . . . . 879 8.1. Stationary and manual speed enforcement. . . . . . . . . . . . . . . . . . . . . . . . . . . 885 8.2. Automatic speed enforcement: Speed cameras. . . . . . . . . . . . . . . . . . . . . . . . 889
x
8.3. Seat belt enforcement. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4. Patrolling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5. Red-light cameras. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.6. Demerit point systems and licence suspension. . . . . . . . . . . . . . . . . . . . . . . . 8.7. Fixed penalties. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.8. DUI legislation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.9. DUI enforcement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.10. Restrictions for DUI-convicted drivers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.11. Treatment of DUI-convicted drivers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.12. Fines and imprisonment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.13. Motor vehicle insurance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
893 899 902 907 913 916 930 935 941 945 949 955
9. Post-Accident Care. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
981
9.0. 9.1. 9.2. 9.3.
Introduction and overview of three measures. . . . . . . . . . . . . . . . . . . . . . . . . Emergency medical services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rescue helicopters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Automatic crash notification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
981 983 990 994 998
10. General-Purpose Policy Instruments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1005 10.0. Introduction and overview of 13 measures. . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1. Organisational measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2. Information for decision-makers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3. Quantified road safety targets and road safety programmes . . . . . . . . . . . 10.4. Safe community programmes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.5. Exposure control. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.6. Land use plans (urban and regional planning) . . . . . . . . . . . . . . . . . . . . . . . 10.7. Road plans and road construction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.8. Road safety audits and inspections. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.9. Motor vehicle taxation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.10. Road pricing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.11. Changes in the modal split of travel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.12. Road traffic legislation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.13. Regulating commercial transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1005 1012 1017 1020 1023 1026 1031 1039 1043 1048 1053 1061 1069 1075 1079
PART III Vocabulary and Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1093 Definitions of Technical Terms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1095 List of Abbreviations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1115 Subject Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1117
xi
PREFACE
The second, revised edition of The Handbook of Road Safety Measures, first published by Elsevier Science in 2004, gives a systematic overview of current knowledge regarding the effects of road safety measures. The book gives state-of-the-art summaries of current knowledge regarding the effects of 128 road safety measures. Since 2004, the introduction part and 65 chapters have been revised and 5 chapters have been added. Easily accessible knowledge on how to prevent traffic injury is in increasing demand, as the number of people killed or injured in road accidents continues to grow on a global basis. It is hoped that this book may serve as a reference manual for road safety professionals in every country. The 2004 edition of the book was published in Spanish in 2006. The book is based on the Norwegian edition of the book, first published in 1982 and continuously updated and expanded since 2001. Work on this book started as far back as 1980. During the whole period from 1980 until now, the endeavour to develop and update the book has been funded by the Norwegian Ministry of Transport and Communications and the Norwegian Public Roads Administration. In recent years, the Swedish Road Administration has been an important contributor as well. The Institute of Transport Economics (TØI) would like to thank these institutions for their financial support and their long-term commitment to this research effort. Without the original Norwegian edition, the current English version could never have been produced. The present edition is the result of the coordinated effort of Chief Research Officer Rune Elvik and researchers Alena Høye, Truls Vaa and Michael Sørensen – all belonging to the Institute of Transport Economics. The final preparation of the manuscript for publication was made by Unni Wettergreen. The points of view expressed in the book are those of the authors and do not necessarily reflect the positions of the funding agencies. Errors and omissions, if any, are the sole responsibility of the authors. Oslo, May 2009 Institute of Transport Economics Lasse Fridstrøm Managing Director
PART I INTRODUCTION
1.
B ACKGROUND 1.1 PURPOSE
OF THE
AND
G UIDE
HANDBOOK
OF
TO
R EADERS
ROAD SAFETY MEASURES
As the title of this book is Handbook of Road Safety Measures, most readers will perhaps expect a handbook to give instructions or advice concerning its main topic, but not all readers will expect the same kind of instructions or advice. It is therefore appropriate to start the book by describing its background and purpose. Although this book is called a ‘handbook’, it does not provide any instructions or advice of a general nature with respect to how best to design or implement road safety measures. The term ‘handbook’ rather denotes a reference manual, a catalogue or an encyclopaedia of road safety measures. Why is this book written and what is its main purpose? The book is written in order to summarise and present in an easily accessible form what is currently known about the effects of road safety measures. A road safety measure is any technical device or programme that has improving road safety as the only objective or at least one of its stated objectives. Road safety measures may be directed at any element of the road system: patterns of land use, the road itself, road furniture, traffic control devices, motor vehicles, police enforcement and road users and their behaviour. This book takes a broad view of what constitutes a road safety measure. It is not limited to a particular class of safety programmes, but tries to cover everything that is intended to improve road safety. A total of 128 road safety measures are included. Improving road safety is, unfortunately, not a concept that has a standard scientific definition. In this book, it refers to a reduction in the expected number of accidents, a
The Handbook of Road Safety Measures Copyright r 2009 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISBN: 978-1-84855-250-0
4
The Handbook of Road Safety Measures
reduction in accident or injury severity or a reduction in the rate of accidents or injuries per kilometre of travel. The main purpose of the book is to describe, as objectively as possible, the effects of road safety measures on road safety. Some road safety measures influence not only road safety but also the ease of travel and the quality of the environment. Ease of travel is a broad concept that includes aspects such as accessibility (the availability of a certain destination for travel), out-of-pocket expenses (like motor vehicle operating costs) and travel time. In this book, the term mobility is used to denote the ease of travel in terms of accessibility, cost and travel time. Environmental impacts of road safety measures refer primarily to impacts on traffic noise and air pollution, but in some cases, other impacts are briefly mentioned, for example, impacts on the working conditions of professional drivers. Some of the terms that have been used to describe the contents of this book, such as ‘current knowledge’ and ‘objective description’, require a more extensive discussion. This will be undertaken in later chapters of Part I (in particular, Chapters 4 and 5). Before describing the main questions, the book tries to answer, its structure and the role of research in promoting road safety, what this book is not intended to be needs to be explained. This book is not a technical design handbook. It does not tell readers how to design a junction or how to build a car. This book does not offer a prescription for road safety policy. It does not tell readers which road safety measures ought to be taken, nor does it instruct policymakers in how to set priorities for the provision of road safety. Section 1.4 outlines how the line separating road safety research from road safety policymaking is understood in the book. This book does not tell you how to do road safety research; however, it tries to assess systematically the quality of current knowledge about the effects of road safety measures. In doing so, this book of course invokes widely accepted standards of technical rigour and quality in applied research. However, assessing the quality of what is known is not the same thing as instructing researchers about how to improve knowledge. This book does not tell readers how to set up an accident recording system or how to investigate accidents, but discusses the concept of accident causation and briefly summarises what is known about factors that contribute to road accidents. Although this presentation may perhaps give readers some ideas about what they should be looking for when trying to find out why road accidents happen, it is highly deficient in acting a guide as to how best to investigate and record road accidents.
Part I: 1. Background and Guide to Readers
5
Some readers may take exception to the consistent use of the word ‘accident’ in the book, preferring perhaps other words like crash or unintentional injury event (Langley 1988). Hopefully, these readers will not be deterred from using the book. Some of the arguments for not using the word ‘accident’ are, we believe, based on misunderstanding. For example, it has been argued that the word ‘accident’ has traditionally been used to represent events that occur at random, and which are therefore unpreventable. This point of view is both correct and incorrect. It is correct in that there is an element of randomness in accident occurrence. However, the occurrence of accidents is never entirely random. Young male drivers are systematically over-involved in road accidents. The gender and age of drivers involved in road accidents are, therefore, not entirely a matter of chance. On the contrary, the occurrence of a specific road accident is random in the absolute sense that if it could have been accurately predicted, it would not have happened (assuming that accidents are not deliberate; that nobody wants to become involved in an accident). Part of the nature of random events is that the precise time and place of their occurrence, as well as the precise nature of their impacts, are unpredictable. But unpredictability in this sense does not necessarily imply un-preventability. To illustrate this, imagine a 100km-long road, chopped up into 100 consecutive 1-km sections. The number of accidents recorded on each 1-km section is counted, and the distribution of accident counts among the 100 sections is found to closely follow the Poisson probability law, which means that accident occurrence in these 100 road sections is random in the sense that it is not statistically possible to identify one road section that has a higher expected number of accidents than any other road section. Yet it hardly follows from this observation that the accidents occurring along the 100-km road cannot be prevented. Suppose, for example, that all drivers using the road slowed down by 10 km per hour. It is very likely that there would then be fewer accidents. Or, suppose road lighting is installed along the road. Again, it is likely that there would be a reduction in the number of accidents. ‘Accident’ is the right word for a road crash, precisely because it connotes randomness. It is a matter of fact that there is a large, but not always dominant, element of randomness in accident occurrence. It is, however, a serious misunderstanding to suggest that randomness as such implies that accidents cannot be prevented.
1.2 WHICH
QUESTIONS DOES THE BOOK ANSWER?
This book provides answers to the following questions:
Which measures can be used to reduce the number of traffic accidents or the severity of injury in such accidents?
6
The Handbook of Road Safety Measures
Which accident problems and types of injury are affected by the different measures? What effects on accidents and injuries do the various road safety measures have according to international research? What effects do the measures have on mobility and the environment? What are the costs of road safety measures? Is it possible to make cost–benefit evaluations of the measures? Which measures give the greatest benefits for traffic safety seen in relation to the cost of the measures?
Not all these questions are equally easy to answer, and it is not always possible to give a precise or conclusive answer. For example, the effect of a measure on accidents may vary from place to place, depending on the design of the measure, the number of accidents at the spot, any other measures that have been implemented, etc. As a result, different studies of the same measure may provide different conclusions. An attempt has been made to identify sources of variation in study findings and to try to form as homogeneous groups as possible when presenting estimates of the effects of measures on road safety. This will be discussed more detail in Chapter 2.
1.3 STRUCTURE
OF THE BOOK
The book consists of three parts, each of which can be read independently. The chapters in each part are also designed to be read independently. Part I describes the purpose of the book and its structure, the method used in surveying and analysing the literature the book is based on, factors contributing to road accidents, basic concepts of road safety research, the quality of road safety evaluation research and scientific approaches to planning and policymaking. Part II describes road safety measures in 10 different areas. Within each area, a number of different types of measures are described in individual sections. The 10 areas are 1. 2. 3. 4. 5. 6. 7. 8.
Road design and road equipment (20 measures) Road maintenance (9 measures) Traffic control (21 measures) Vehicle design and protective devices (29 measures) Vehicle and garage inspection (4 measures) Driver training and regulation of professional drivers (12 measures) Public education and information (4 measures) Police enforcement and sanctions (13 measures)
Part I: 1. Background and Guide to Readers
7
9. Post-accident care (3 measures) 10. General purpose policy instruments (14 measures). Part III contains a glossary of words, symbols and abbreviations, which are used in the book and a subject index. In Part II, each chapter and each of the sections within each chapter has been written following the same structure. The first section in each chapter gives an overview of the amount of research available and summaries of the effects on accidents, environment and mobility, as well as an overview of costs and cost–benefit analyses. The sections that described specific types of road safety measures all consist of the same subsections, a short description of which is given in the following. Problem and objective. This section describes the road safety problem, which the measure is designed to solve or reduce. A road safety problem can be described in terms of a high number of accidents, a high accident rate or a high proportion of serious injuries. For example, it is widely seen as a problem that pedestrians and cyclists are more often involved in injury accidents per kilometre travelled in traffic than car occupants, and that they tend to be more seriously injured than car occupants when involved in an accident. As far as possible, the size of the road safety problem which each measure is intended to affect is shown by means of accident figures or estimates of risk. However, not all road safety problems can be described exhaustively in numerical terms only. This applies, for example, to the feeling of insecurity that some road users experience. Many road safety measures are intended to tackle local problems, having a fairly clearly limited scope in time and space. However, this does not apply to all measures. Some measures are directed towards more general problems, which may affect all road users and all places. In such cases, it is difficult to state precisely the number and nature of accidents which these measures are designed to affect. For some road safety measures, the concept of ‘target accidents’ is thus somewhat ill defined (Hauer 1997). Description of the measure. This section gives information concerning the design of a road safety measure and its intended function. Detailed technical descriptions are not given. Illustrations showing the measure are given in some cases. Effect on accidents. This section deals with the effects on accidents, or on the severity of injury in accidents, which have been found in research. Whenever possible, effects are stated in terms of the percentage change of the number of accidents or injuries attributable to a certain measure. All estimates of effect presented in this book are uncertain. The most important sources of such uncertainty are identified for each
8
The Handbook of Road Safety Measures
measure. Statistical uncertainty is stated in terms of a 95% confidence interval for the estimate of effect. For measures where no studies have been found that quantify effects on road safety, the effect is described in other ways. Effect on mobility. In addition to the effect on accidents and injuries, many road safety measures also have effects on mobility. These impacts are briefly described, but not in as great detail as safety effects. Effects on the environment. Effects on the environment are briefly described. Such effects include traffic noise and air pollution in a wide sense. Major incursions into the landscape and changes in land use should also be regarded as important environmental effects. Costs. For the majority of measures, information is given regarding the cost of the measure. The information is taken partly from official budgets and accounts, partly from research reports and partly from producers or dealers in safety equipment. Good estimates of cost have not always been found. The cost figures presented are usually an estimate of the average cost for a ‘unit’ of a measure, for example, 1 km of track for walking and cycling, one roundabout, one signalised junction, one seat belt, one set of ABS brakes, etc. In addition, total costs are presented for measures whose extent of usage is sufficiently well known. Cost–benefit analysis. Examples are given of cost–benefit analysis of most measures. It is important to bear in mind that the results of cost–benefit analyses depend strongly on the context to which they refer. Monetary valuations of impacts, which are a key element of cost–benefit analysis, vary substantially between countries. As a rule, one would therefore not expect the results of cost–benefit analyses made in one country to apply directly to another country. The context to which most of the analyses presented refer is the current situation in Norway. However, where cost– benefit analyses have been reported in other countries, they are quoted. The applicability of cost–benefit analyses to road safety measures is discussed in detail in Chapter 6 of Part I.
1.4 SCIENCE
AND POLITICS IN ROAD SAFETY
Road safety research, in particular road safety evaluation research, is highly applied. This type of research is carried out mostly to help reduce the number of road accidents and the injuries resulting from them. Can science and politics be kept apart in such a highly applied field of research? Where is the dividing line between science and politics in road safety?
Part I: 1. Background and Guide to Readers
9
A distinction can be made between three types of issues that arise in policymaking. The three types of issues can be stated in the following terms:
Normative: A is a good thing (or the right thing to do). Empirical: If action B is performed, A will be produced. Prescriptive: Therefore, we ought to take action B.
Normative issues are about deciding what we think is good or right and are ultimately matters of moral judgement. Most people would probably agree that reducing traffic injury is a good outcome. Hence, most people would probably also endorse a policy objective stating that traffic injuries should be reduced. Formulating the ideals and objectives that policy should strive to realise clearly lies within the realm of politics rather than science. Policy objectives represent human value systems and seek to articulate these in an attractive way. Does this mean that science has nothing to say about normative issues? No. A scientific evaluation of the solutions proposed to normative issues can be made by relying on principles of logical consistency. For example, a policy objective stating that every road user has the right to safer travel than the average risk faced by road users can be rejected as logically inconsistent, since it is impossible for everyone to be safer than average. A broader scientific analysis of human value systems belongs to ethics and moral philosophy, and is outside the scope of this book. The main topic of road safety evaluation research is to determine whether road safety measures are effective in improving road safety. This is entirely an empirical issue. It was stated in Section 1.1 that this book describes, as objectively as possible, what is known about the effects of road safety measures, in particular their effects on road safety. What does this statement mean? How can any description of knowledge claim to be objective? Objectivity is not something that can be meaningfully measured in numerical terms. It is, however, an ideal of science to which this book strives by
seeking to present objective knowledge about the effects of road safety measures, assessing knowledge according to standards of validity that are independent of the content of that knowledge, and depend solely on how it was produced, and refraining from advocacy.
Let us elaborate on each of these points. Objective knowledge. In discussing what we mean by scientific knowledge, epistemology has traditionally relied on a subjective conception of knowledge, in which knowledge is
10
The Handbook of Road Safety Measures
defined as justified true belief. Within this framework, knowledge cannot exist without a knowing subject. In short, a justified and true statement does not constitute knowledge unless someone is aware of the statement and believes it. This conception of knowledge lies close to everyday usage of the term. Hauer, for example, in discussing the state of knowledge with respect to the effects of road safety measures, states (1988, 3): ‘My own critical views about the amount of factual knowledge that is available in the field of road safety delivery rest on years of study. As I moved from one inquiry to another and began to notice how shallow are the foundations of what passes for knowledge, I gradually realized that ignorance about the safety repercussions of the many common measures is not the exception.’ Three years later, he remarked (Hauer 1991, 135): ‘How little we know about the safety consequences of our road design decisions and about the repercussions of our traffic control actions is simple to demonstrate. One needs only to ask the engineer: ‘‘Approximately how many accidents per year do you expect to occur with design X?’’ While the engineer might venture an opinion, in truth, the arsenal of knowledge at the disposal of the North American engineer just does not suffice to give an answer.’ While conforming both to everyday usage and the traditions of epistemology, the subjective concept of knowledge creates a number of difficulties. Although it makes sense to say that person A knows more about a subject than person B, if person A can pass a more difficult examination about the subject than person B, it hardly makes sense to say that the amount of knowledge that is available to the general public concerning a subject is determined primarily by how much person A can remember when undergoing an examination in the subject. Karl Popper introduced the concept of objective knowledge (Popper 1979), which he defines (1979, 73) as ‘the logical content of our theories, conjectures, guesses’. He adds that ‘Examples of objective knowledge are theories published in journals and books and stored in libraries; discussions of such theories; difficulties or problems pointed out in connection with such theories, and so on.’ Knowledge in the objective sense, according to Popper (1979, 109), is knowledge without a knower; it is knowledge without a knowing subject. In short, the concept of objective knowledge can be defined as all results of research, theoretical or empirical, that are available to the general public by virtue of being written or otherwise stored in a medium that is accessible to anyone who wants to learn its contents. Knowledge in this sense exists, as pointed out by Popper, in the shelves of libraries and archives. This kind of knowledge is objective in the sense that it exists irrespective of whether anyone keeps it inside his or her head. It is, however, not necessarily objective in the sense that everyone who reads a certain paper in a journal
Part I: 1. Background and Guide to Readers
11
will find the results reported in the paper convincing and therefore believe them, as required according to the subjective conception of knowledge. This book seeks to develop objective knowledge about the effects of road safety measures by relying on an extensive and systematic search of the literature, described in detail in Chapter 2, and by summarising this literature by means of formal techniques of meta-analysis that minimise the contribution of subjective factors that are endemic in traditional, narrative literature surveys. Assessing the validity of knowledge. Can the results of road safety evaluation studies be trusted? Do these studies always show the true effects on road safety of the measures that have been evaluated? Regrettably, the answer to these questions is no. Hauer (2002, 3) laments: ‘By publishing many biased accounts on a variety of treatment, all giving inflated estimates of safety effect, one creates an entirely incorrect lore about what is achievable. . . . The publication of incorrect results is like the release of toxin into a pristine body of water. It does not take much to make an entire lake unfit for drinking. . . . The remedy to knowledge pollution is not reader education. While it is useful to educate potential readers to assess critically the results of safety studies, it is too much to hope that reader education can undo the damage done by publishing poorly done research.’ In this book, a systematic framework has been used to assess the validity of the studies that are quoted. This framework applies to published or at least written studies, and not to oral communications, personal beliefs, tacit knowledge or other forms of subjective knowledge. Checking studies according to a set of criteria of validity may be regarded as an overly restrictive and simplistic way of assessing the validity of knowledge. Three points can be made in defence of this approach. First, the set of criteria for assessing the validity of evaluation studies are intended as normative criteria, not as descriptive criteria. All too often, controversies about research revolve around the contents of the results, rather than the methodological rigour of the research, and are heavily influenced by vested interests, rather than a disinterested search for the truth (see Crossen 1994, for some striking examples of these tendencies). Second, it is conceded that a set of normative criteria is bound to be incomplete, in the sense that it does not exhaust the considerations that are regarded as relevant in assessing the validity of studies. Some considerations about study quality may apply just to one particular study and are thus not easily stated in general terms. Third, while an informal and subjective assessment of the validity of research can reflect considerations that are difficult to formalise, it is nevertheless likely to be subject to more or less unknown biases. No matter how hard we try to be objective, there is
12
The Handbook of Road Safety Measures
always a risk that we go by the rule that ‘bad studies are . . . those whose results we do not like’ (Rosenthal 1991, 130). By assessing validity in terms of formally stated, normative criteria, the role of personal prejudices in the assessment can be minimised. Refraining from advocacy. Suppose an effective remedy for road accidents is found. Surely that is good news. Let us apply the remedy at once. Advocacy in research reports refers to statements recommending or calling for the use of specific road safety measures. To offer policy recommendations is to engage in advocacy. While advocacy may be tempting to many researchers (‘Hey look, I’ve found a wonderful solution to an important social problem! Please give me some applause’), it is a temptation that should be resisted. Let us explain why. In the first place, advocacy will, at least in the long term, undermine the confidence in research. Many road safety measures are controversial. The fact that a certain road safety measure is effective does not always mean that people like it. A researcher who has repeatedly advocated lower speed limits to improve road safety will find his credibility greatly reduced next time he publishes a study that, once again, concludes that lowering speed limits is an effective way of improving road safety. In the second place, there is nearly always more than one way of improving road safety. Treatment A may be effective for a particular accident problem, but so are treatments B, C, D, E and F. To choose between these treatments, policymakers need to know more than simply the fact that they are all likely to reduce the number of accidents. Perhaps costs differ greatly. Perhaps the impacts on mobility and the environment are different. Perhaps public opposition is strong to three of the measures, but not to the other three. And so on. In short, making road safety policy involves complex trade-offs that tend to be overlooked by those who advocate a particular road safety measure. In the third place, to advocate something one should really be sure that it works. If knowledge is not firmly established, one can get nasty surprises when introducing a treatment that was erroneously believed to be effective. Unfortunately, knowledge about the effects of road safety measures is not always very firmly established. Some readers may object to these arguments by saying that this book offers covert policy recommendations by presenting cost–benefit analyses of the road safety measures it covers. However, a cost–benefit analysis is not a policy recommendation. It is simply a way of showing, in terms of a common scale, the relative importance of various impacts of a programme. Trying to identify the practical implications of a cost–benefit analysis is not as straightforward as some people think. It is not the case that an action should always be adopted if the benefits of that action are greater than
Part I: 1. Background and Guide to Readers
13
its costs, and should never be adopted if the costs are greater than benefits. This point is made in virtually every textbook on cost–benefit analysis. Moreover, it is not obvious that road safety policy can or ought to be based slavishly on the results of cost–benefit analyses. To determine the weight that cost–benefit analysis should carry in road safety policy requires judgements that must be made outside the framework of cost–benefit analysis, and are not part of the analysis as such.
2.
L ITERATURE S URVEY 2.1 SYSTEMATIC
AND
M ETA -A NALYSIS
LITERATURE SEARCH
A comprehensive survey of studies evaluating the effects of road safety measures has been made. These studies have been identified by means of a systematic literature search. This section describes how the literature search was done. The literature search consists of a ‘fixed’ part and a ‘variable’ part. The fixed part is a comprehensive search for studies in a sample of sources. The variable part is based on the results of the fixed part of the search. This approach is sometimes referred to as the ancestry approach. The fixed part of the literature search is a systematic survey of the following main groups of sources:
Previous Norwegian editions of Handbook of Road Safety Measures Scientific journals Reports issued by selected research institutes Conference proceedings from a sample of regular conferences The library of the Institute of Transport Economics Bibliographical databases.
The variable part of the literature search comprises references found in studies that were retrieved from these sources. Previous Norwegian editions of Handbook of Road Safety Measures. Previous editions of this book have been published in Norwegian and in English. The previous editions
The Handbook of Road Safety Measures Copyright r 2009 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISBN: 978-1-84855-250-0
16
The Handbook of Road Safety Measures
of the book (Pedersen, Elvik and Berard-Andersen 1982, Elvik, Vaa and Østvik 1989, Elvik, Mysen and Vaa 1997, Elvik and Vaa 2004) have been examined, and we have tried to obtain studies to which references were made. No studies that have been referred to in the earlier editions of the book have been omitted. Even though the first edition of the book refers to many studies that by now are relatively old (over 30 years), none of these studies have been omitted. There are two main reasons for this. First, by keeping old studies, one has the opportunity of finding whether new and old studies reach the same conclusions. Second, the research is cumulative. This means that new studies are based on and add to the results of older studies, but attempt to refine, confirm, falsify, or develop these results by replicating studies or by applying better research methods. Scientific journals. A number of scientific journals has been hand-searched and relevant papers have been identified. Table 2.1 shows the journals that have been searched and the volumes included for each journal. The journals that were judged to be the most important have been examined from around 1970 or from the first published volume. Less important journals have been searched from 1980. Highway Research Record ceased publication in 1974 and was replaced by Transportation Research Record. Reports issued by research institutes. Reports issued by a number of research institutions and public agencies in different countries have been searched. Table 2.2 shows the institutions whose publications have been systematically surveyed in the literature search. Volumes included for the different series of reports issued by these institutions largely cover the period for which the report series in question has been in existence. For report series that were regarded as less important, only volumes from after 1980 have been studied. Conference proceedings. Every year, or at other fixed intervals, a number of international conferences or seminars are held that deal with the questions of road safety. Normally, conference proceedings, which contain the contributions to these conferences, are published. For conferences that are held regularly, the proceedings from conferences in recent years have been searched systematically. Table 2.3 shows the conferences concerned. In addition to these regular conferences, a number of other conferences are held. Proceedings of these conferences have been obtained if there was reason to believe they might contain relevant papers.
Part I: 2. Literature Survey and Meta-Analysis
17
Table 2.1: Scientific journals surveyed as part of the literature search Journal
Volumes included
Accident Analysis and Prevention
1969–
Australian Road Research (ceased publication in 1991)
1970–91
Dansk Vejtidsskrift (Danish Road Journal)
1980–
Ergonomics
1980–
Highway Research Record (ceased publication in 1974)
1960–74
Human Factors
1980–
IATSS Research
1980–
ITE-Journal (formerly Traffic Engineering)
1970–
Journal of Risk and Uncertainty
1988–
Journal of Safety Research
1969–
Journal of Traffic Medicine
1974–
Journal of Transport Economics and Policy
1970–
Journal of Transportation Engineering
1970–
Nordic Road and Transport Research
1989–
NTR-nytt (News from Nordic Research)
1992–
Policy Sciences
1980–
Public Roads
1980–
Recherche-Transports-Se´curite´ (RTS – INRETS Research Review)
1984–
Risk Analysis
1981–
Samferdsel
1970–
Safety Science (formerly Journal of Occupational Accidents)
1980–
Strassenverkehrstechnik
1980–
Traffic Engineering and Control
1970–
Transportation Research Part F
1998–
Trafikken og Vi
1970–
Transportation Research (series A and B)
1980–
Transportation Research (series C)
1993–
Traffic Injury Prevention
1999–
Transportation Research Record (replaced Highway Research Record)
1974–
Zeitschrift fu¨r Verkehrssicherheit
1970–
Literature search in the library of the Institute of Transport Economics. Literature searches have been made in the library of the Institute of Transport Economics using subject words. These searches were done on a supplementary basis, designed to identify studies that were not found in the other sources that were searched systematically. Bibliographical databases. Literature searches have been carried out using several international bibliographical databases. These are ROADLINE at VTI (Swedish Road
18
The Handbook of Road Safety Measures
Table 2.2: Institutions (listed alphabetically) whose publications have been searched in literature survey Institution
Period covered
Australian Road Research Board (ARRB, Australia)
1970–
Beratungsstelle fu¨r Unfallverhu¨ting (BFU, Switzerland)
1980–
Bundesanstalt fu¨r Strassenwesen (BASt, Germany)
1974–
Danmarks Transportforskning (DTF)
2001–
Kommunikationsforskningsberedningen (KFB, TFB, TFD, Sweden)
1977–
Lunds Tekniske Høgskole (Lund Institute of Technology, Sweden)
1977–
Nordisk Ministerra˚d (Nordic Council of Ministers, Nordic countries)
1973–
Nordisk Vegteknisk Forbund (NVF, Nordic Road Federation, Nordic countries)
1970–
Organization of Economic Cooperation and Development (OECD)
1970–
Ra˚det for Trafiksikkerhedsforskning (Danish Council for Road Safety Research, Denmark)
1969–2001
SINTEF Samferdselsteknikk/NTH Samferdselsteknikk (Norwegian Institute of Technology, Norway)
1975–
Society of Automotive Engineers (SAE, USA)
1980–
Statens vegvesen (Public Roads Administration, Norway)
1980–
Statens Va¨g- och Trafikinstitut (VTI, Swedish Road and Transport Research Institute, Sweden)
1975–
SWOV (Institute for Road Safety Research, The Netherlands)
1970–
TØI (Institute of Transport Economics, Norway)
1963–
Transport Research Laboratory (TRL, TRRL, RRL, Great Britain)
1965–
US Department of Transportation (USA)
1980–
US Transportation Research Board (TRB, USA)
1960–
Vejdirektoratet (Public Roads Administration, Denmark)
1980–
Va¨gverket (National Roads Administration, Sweden
1980–
Table 2.3: Conference proceedings which have been studied as part of the literature search Conference (frequency) Alcohol, Drugs and Traffic Safety (every 3 years)
Year 1971–
Australian Road Research Board Conference (every second year)
1980–
PTRC Summer Annual Conference (now: European Transport Forum, annual)
1985–
Road Safety in Europe (VTI et al.) (every second year)
1985–
Road Safety on Four Continents (VTI and TRB) (every second year)
1985–
TRB Annual Meeting (annual)
1985–
VTI/TFBs Research Days (annual)
1989–
Part I: 2. Literature Survey and Meta-Analysis
19
and Transport Research Institute), OECD’s database IRRD, the database TRANSPORT (Silverplatter), Sciencedirect (the online database from Elsevier), PubMed (of the US National Library of Medicine) and the Cochrane Library. A large number of road safety evaluation studies have been found in the sources listed above. Many of these studies refer to other studies, which were obtained if the references appeared to be relevant. Relevance was judged according to study titles and abstracts (if available). This approach to searching the literature does not guarantee 100% coverage. We do believe, however, that we have retrieved a large proportion of the best road safety evaluation research that has been published.
2.2 CRITERIA
FOR STUDY INCLUSION
The main objective of the literature search was to find studies that have quantified, or at least have tried to quantify, the effect of one or more road safety measures on the number of accidents, accident rate and the number of injuries or risk of injuries. Studies that have evaluated the effects of road safety measures by relying on proxy measures for safety, such as conflicts between road users or changes in road user behaviour, rather than accidents or injuries, are less relevant. One reason for this is the fact that for many forms of behaviour, the relationship to accident occurrence is unknown. Another reason is that the ultimate objective of all road safety measures is to reduce the expected number of accidents or injury severity. This does not mean that measurements of road user behaviour, for example, are not of interest. On the contrary, they can make a study more valuable by supplementing accident records. For example, the validity of a study is greater if it describes changes in both speed and accidents – and shows that these changes are closely related to each other – than if an otherwise similar study provides information only on speed or accidents by itself.
2.3 STUDY
CLASSIFICATION
Studies have been classified according to the road safety measure whose effects they have evaluated. Some studies have evaluated several measures and are therefore included for each of the measures evaluated. However, the majority of studies evaluate the effects of just one road safety measure. It has traditionally been regarded as a strength if a study tried to evaluate the effects of a particular road safety measure. However, as far as road safety policy is concerned,
20
The Handbook of Road Safety Measures
several measures are usually combined in one programme. In that case, it is important to know not just the effects of each measure that goes into the programme but the combined effects of all measures put together. It is not obvious that the effects of a road safety programme will be equal to the sum of the effects of the individual measures that make up the programme. The effect of a measure will not necessarily be the same when it is implemented in combination with other measures, as when it is implemented on its own. Another general limitation of road safety evaluation research is that it often requires that the measures are implemented fairly extensively to provide enough data to evaluate effects. This means that evaluation research does not always provide a good basis for predicting the effects of new measures. Those who develop new measures would like to be able to predict the effects of the measures before they are introduced. Such prediction is not always possible. In Chapter 5, the possibility of giving a theoretical account for the findings of road safety evaluation research will be discussed.
2.4 THE
USE OF META-ANALYSIS TO SUMMARISE STUDY RESULTS
The results of studies that have evaluated the effects on accidents and injuries of different measures are summarised by means of meta-analysis, provided it is applicable. Meta-analysis is a quantified synthesis of results of several studies that have evaluated the same road safety measure stated in the form of a weighted mean estimate of effect (Elvik 1999). As a part of the meta-analysis, moderating factors are investigated that influence the size of the effect of a road safety measure on accidents or injuries. There are a number of textbooks on meta-analysis (Cooper and Hedges 1994, Petitti 2000, Lipsey and Wilson 2001) that describe various techniques in detail. Here, only the main elements are described to help readers understand the results that are presented in the individual chapters. Main elements of meta-analysis. The study unit in a meta-analysis is a result, or an estimate of effect. An estimate of effect has to be stated as a precise point estimate in order to be included in a meta-analysis. If a result is stated simply as: ‘No statistically significant changes in the number of accidents were found’, it cannot be included in a meta-analysis. Moreover, the standard error of an estimate of effect has to be known, at least if results are to be weighted according to their statistical precision. A single study can contain more than one result. In such cases, all results, or the most important results from studies with a very large number of results, have been included in the meta-analyses. Multiple results from the same study have been treated as statistically independent, although this assumption may not always be correct.
Part I: 2. Literature Survey and Meta-Analysis
21
Study results can be summarised by means of meta-analysis if the studies
provide at least one numerical estimate of the effect of a road safety measure, or provide information that can be used to derive such an estimate and state the number of accidents on which the estimate of effect is based or provide other information that allows the calculation of the statistical uncertainty of the effect estimate, such as the confidence interval.
Basics of the log odds method of meta-analysis. The log odds method of meta-analysis has been applied throughout (Fleiss 1981, Shadish and Haddock 1994). According to this method, a weighted mean estimate of effect is calculated on the basis of the estimates of effect found in the studies that have been retrieved. This method of metaanalysis was chosen because the odds ratio (OR) is the most commonly found estimate of effect in road safety evaluation studies. An example of how an OR is calculated is as follows: If a study finds that there were 75 accidents on road X before a measure was implemented, and 23 accidents afterwards, whereas on a comparison road, there were 67 before the implementation of the measure on road X and 25 afterwards (no measure was implemented on the comparison road), the OR is (23/75)/(25/67) ¼ 0.307/ 0.373 ¼ 0.822. This corresponds to an accident reduction of 17.8% (1 þ 0.822). In studies that employ multivariate techniques of analysis, effects are normally stated in terms of an OR that has been adjusted for confounding. When applying the log odds method of meta-analysis, a summary effect is calculated as the weighted mean of the logarithms of the individual estimates of effect (ORs). Combining logarithms of ORs yields an unbiased estimate of the weighted mean effect of a set of studies. The steps in a log odds meta-analysis are
calculation of estimates of effect, calculation of statistical weights and choice of the model of meta-analysis: Fixed effects when there is no systematic variation in the estimates of effect or random effects when there is systematic variation in the estimates of effect, calculation of summary estimates of effect, and confidence intervals: for each summary effect, a 95% confidence interval is calculated.
Calculation of estimates of effect. Estimates of effect are calculated as ORs. Some of the estimators of effect commonly found in road safety evaluation studies are listed in Table 2.4. The list is not exhaustive. Estimates of effect based on coefficients produced by multivariate analyses, which have the statistical properties of ORs, are not as common, but have increasingly been used in recent studies. The different estimators of effect should not be mixed up. Producing summary estimates of effect in meta-analysis
22
The Handbook of Road Safety Measures
Table 2.4: Commonly used estimators of effect in road safety evaluation studies Name of dependent variable
Formal definition
Odds
Uat/Ubt
Odds ratio (simple or adjusted)
(Uat/Ubt)/(Uac/Ubc)
Ratio of odds ratios
[(Uati/Ubti)/(Uaci/Ubci)]/[(Uatj/Ubtj)/(Uacj/Ubcj)]
Ratio of relative risk
[Uati/(UatiþUbti)]{[Uatj/(UatjþUbtj)]
Accident rate ratio
(Ua/Ta)/(Ub/Tb)
U ¼ number of accidents, T ¼ traffic volume, exposure to risk, a ¼ after, or with, some measure whose effect is evaluated, b ¼ before, or without, some measure whose effect is evaluated, t ¼ test group, c ¼ comparison group, i ¼ category I, j ¼ category j.
based on studies that employ different estimators of effect can be misleading because both the statistical properties and the substantive interpretations of the various estimators differ. When other estimates of effect other than ORs are reported, ORs are calculated as far as possible based on the available information. Calculation of statistical weights and choice of model. There are two methods of combining estimates of effect in meta-analysis, the fixed effects model and the random effects model. The fixed effects model of analysis is based on the assumption that there is no systematic variation in effects in the set of studies considered, that is, all estimates of effect are samples of the same ‘true’ effect. When there is systematic variation, or heterogeneity, in the estimates of effect, the estimates cannot be regarded as representing the same ‘true’ effect. In this case, a random effects model is more adequate. In a random effects model, an account is taken of heterogeneity in the results and an underestimation of the uncertainty of the summary effect is avoided. The differences between the fixed effects and the random effects models can be summarised as follows: The fixed effects model is adequate only if there is no heterogeneity in the results. Otherwise it will assign too much weight to results with large statistical weights and the confidence interval of the summary effect will be underestimated. The random effects model can be applied whether or not there is heterogeneity in the results. When there is significant heterogeneity, it assigns relatively less weight to results with large fixed effects weights, and confidence intervals of summary effects are larger than that in the fixed effects model. The less heterogeneity there is in the estimates of effect, the more similar will be the results from the two models. When applying fixed effects and random effects models in meta-analysis, they differ with respect to how the statistical weights are calculated. In the fixed effects model, the
Part I: 2. Literature Survey and Meta-Analysis
23
statistical weight of the natural logarithm of each effect estimate is the inverse of its variance: wi ¼
1 vi
The variance of the logarithm of the OR is vi ¼
1 1 1 1 þ þ þ A B C D
where A, B, C and D are the four numbers that enter the calculation of the estimate of effect. In studies that do not use comparison groups, the terms 1/C and 1/D drop out. The same applies to studies that state the effects of a road safety measures in terms of an accident rate ratio. Statistical weights are estimated on the basis of the recorded number of accidents. In case of zero accidents, 0.5 is added to all four (or two) numbers used in estimating the statistical weight of a result. In a random effects model, the statistical weights are calculated as a function of the fixed effects weights and a measure of the heterogeneity in the estimates of effect. The more heterogeneity there is in the results, the more similar will the statistical weights of the estimates of effect become, that is estimates based on large fixed effects weights will have their weights adjusted more than estimates based on small fixed effects weights. In order to test the amount of heterogeneity in the estimates of effect, the following test statistic, Q, is estimated: g 2 P w i yi g X wi y2i i¼1g Q¼ P i¼1 wi i¼1
where yi is the estimate of effect i and wi the fixed effects weight of estimate i. This test statistic has a w2 distribution with g1 degrees of freedom, where g is the number of estimates of effect that have been combined. If this test statistic is statistically significant, a random effects model is more adequate than a fixed effects model. In a random effects model, the statistical weights are modified to include a component reflecting the systematic variation of estimated effects between cases. This component is estimated as follows (Shadish and Haddock 1994): t2 ¼
Q ðg 1Þ C
24
The Handbook of Road Safety Measures
Q is the test statistic described earlier, g the number of estimates and c the following estimator: 2P 3 g 2 w g X 6i¼1 i 7 7 c¼ wi 6 g 4P 5 i¼1 wi i¼1
The variance of each result now becomes vi ¼ t2 þ vi The corresponding statistical weight becomes the inverse of the variance. Random or fixed effects? Most meta-analyses that are presented in the book have been calculated based on a random effects model. Fixed effects models have been applied only when too few estimates of effect are available for calculating a random effects model. In meta-analyses that have not been updated after 1997, the fixed effects model is the most commonly used model. Summary effects. The weighted summary effect based on a set of g estimates is calculated as follows: 0P 1 g wi yi Bi¼1 C C y ¼ expB g @ P A wi i¼1
where ‘exp’ is the exponential function (i.e., 2.71828 raised to the power of the expression in parenthesis), yi the logarithm of each estimate of effect and wi the statistical weight of each estimate of effect. Confidence intervals. A 95% confidence interval for the weighted mean estimate of effect is obtained according to the following expression: 2 3 0P 1 g 6 7 wy 6Bi¼1 i i C 1:96 1 7 B C 6 7 ffiffiffiffiffiffiffiffiffiffiffi s 95% confidence interval ðupper=lower limitÞ ¼ exp6@ g A 7 g P 4 Pw 5 i wi i¼1
i¼1
The weights in this expression are either the fixed effects weights or the random effects weights, depending on the model of analysis adopted.
Part I: 2. Literature Survey and Meta-Analysis
2.5 DOES
25
A WEIGHTED MEAN ESTIMATE OF EFFECT MAKE SENSE?
A concern that many people have about meta-analysis is the so-called apples and oranges problem. This refers to the fact that studies that may differ greatly among themselves are combined into an overall estimate of the average effect of a road safety measure. It is argued that this does not make sense if studies are very heterogeneous, for example, with respect to different versions of the measure, countries or methods used in the studies. Fortunately, the relevance of this argument can to some extent be tested in a metaanalysis. By doing so, one gains an impression of how meaningful it is to generalise a set of findings of evaluation studies in terms of a weighted average result. A way of checking whether a weighted mean estimate of effect makes sense is to prepare a funnel graph plot. An example of such a graph is shown in Figure 2.1. The graph shows 94 results of studies that have evaluated the effects of road lighting on the number of accidents. The horizontal axis shows the natural logarithms of the estimates of effect. Values below 0 mean that the number of accidents is reduced, the
700
Summary effect (fixed effects): -0.194 Arithmetic mean: -0.292
Statistical weight (fixed effects)
600
Median: -0.319 500 400 300 200 100 0 2.0
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
-2.0
-2.5
-3.0
Effect estimate (natural logarithm; 0 = no effect)
Figure 2.1: Funnel graph of studies that have evaluated the effects of road lighting on the number of accidents (unspecified severity).
26
The Handbook of Road Safety Measures
value 0 means that the number of accidents is unchanged and values above 0 mean that the number of accidents increases. The vertical axis shows the statistical weight (fixed effects) of the results. The greater the statistical weight, the more the accidents which form the basis of a result. The dots indicate the individual results. Furthermore, three measures of the main tendency of the results are shown: the median, the arithmetic (unweighted) mean and the summary effect that has been calculated with the fixed effects model. By studying such funnel graphs, an informed opinion can be formed of how reasonable a weighted mean result is. Properties of the distribution of estimates of effect that are investigated based on the funnel graph are the modality and dispersion of the results, the skewness and the sensitivity to outliers. Modality and dispersion of the results refers to the shape of the distribution of estimates of effect and how many humps or peaks it has. Figure 2.1 shows a unimodal distribution, that is, a distribution where the data points gather round a single peak. In this type of distribution, the weighted summary effect lies close to the highest peak of the distribution and thus is representative of the centre of gravity of the distribution. A bimodal distribution is one that has two peaks. In this type of distribution, the average will often lie between the two peaks and thus will not really be very informative. If possible, bimodal distributions should be divided into two, and an average should be calculated for each mode. There may also be distributions with no clear pattern at all, randomly scattered distributions. In these types of distributions, the results are highly dispersed, with no clear tendency in any direction. An average may then be arbitrary and any differences concealed as a result of arbitrary assignment would be important to highlight. Ideally, the distribution of the results should not only be unimodal but also exhibit a systematic pattern where the results that have the largest statistical weights are closest to the mean and results that are further away from the mean have smaller statistical weights. It is not always easy to see if the results follow an ideal distribution or not. There are statistical methods for investigating the distribution of the results and for treating results that are not ideal. First, heterogeneity can be tested statistically as has been described earlier, and a random effects model can be applied that takes into account heterogeneity. A random effects model takes into account that there is heterogeneity, but does not explain it. Second, there are possibilities for explaining heterogeneity. The simplest way is to divide results into sub-groups and to calculate new summary effects for each of the subgroups of results. Results may be grouped, for example, according to injury severity or
Part I: 2. Literature Survey and Meta-Analysis
27
variants of the measure. When summary effects differ between sub-groups, and when heterogeneity is reduced within the sub-groups, the sorting variable is likely to have contributed to the heterogeneity. It is then called a moderator variable. Heterogeneity can also be explained by using meta-regression. In meta-regression analysis, regression models are developed on study level with the estimates of effect as dependent variable and characteristics of the studies as predictors. Characteristics of the studies may be the same variables as the sorting variables in the sub-group analysis (e.g., type of measure investigated, type of roads, methodological aspects, and so on). Thereby, it is possible to investigate which characteristics of the studies affect the outcome of the studies, while controlling for several factors at the same time. One restriction of meta-regression is that it requires quite large numbers of estimates of effect. As a rule of thumb, there should be at least 10 estimates of effect for each predictor included in the model. When there are few estimates of effect, the results may be arbitrary and highly sensitive to, for example, adding or omitting predictors or individual estimates of effect from the analysis. A third possibility that should be considered in some cases is to refrain from calculating a summary effect. When the distribution of estimates of effect is highly heterogeneous without showing any signs of unimodality, a summary effect would not be meaningful. Indications for such a distribution are results that are highly different between the fixed effects and the random effects model and extremely large confidence intervals in the random effects model. This is illustrated by a numerical example in which six estimates of effect have been generated that have a highly heterogeneous and non-unimodal distribution. In this example, the result from the fixed effects model is a summary effect of 57% (95% confidence interval [58; 55]), and that from the random effects model is a summary effect of þ6% (95% confidence interval [62; þ195]). Skewness in a distribution refers to how the data points are distributed around the average, that is, how the individual results distribute themselves around a weighted average result. Ideally, the distribution should be symmetrical around (the natural logarithm of) the summary effect. If a distribution is very skew, the mean will give a misleading impression of where the majority of the results lies. An indication of skewness is a large difference between the median and the arithmetic mean of the distribution. An unskewed distribution will have very similar median and arithmetic mean. Publication bias is one possible source of skewness. Publication bias means that studies are more likely to be published when the results are in accordance with the expectation. In most accident studies, the expectation is that one will find accident reductions following the implementation of a safety measure. Publication bias leads to a skewed
28
The Handbook of Road Safety Measures
distribution of the estimates of effect because there will be fewer results on its ‘undesired’ side. Moreover, results (also in the desired direction) are more likely to be significant when based on large numbers of accidents. Results from small studies that find large effects in the expected direction will therefore be over-represented, and results from small studies that are unexpected will be under-represented. In the absence of publication bias (and other biases), the distribution will be symmetrical. When there is publication bias, the summary effect is likely to show larger accident reductions than would be the case if all studies had been published, and none had been omitted because of insignificant or unexpected results. A statistical possibility for controlling for publication bias is the trim and fill method. This method simulates studies that are assumed to have been suppressed by a tendency to publish (large) effects in the expected direction (Christensen 2003). The distribution of all original estimates of effect and the simulated estimates of effect that are generated in a trim and fill analysis is symmetrical around the peak of the original distribution. The simulated estimates of effect are mostly effects from small studies in the unexpected direction. A summary effect is then calculated based on all, original and simulated, estimates of effect. This summary effect is usually less favourable for the measure that has been evaluated than the summary effect that is based on the published estimates only. The trim and fill method can be applied both to a fixed effects and a random effects model of meta-analysis. An example is shown in Figure 2.2. Figure 2.2 shows the results of the same studies as in Figure 2.1. The ‘funnel’-shaped dotted lines (which are drawn by hand, not fitted to the data) indicate roughly the outer limits of the distribution of the estimates of effect. All original estimates of effect are located inside these lines and seem to be almost symmetrically distributed. All the same, 15 new data points have been generated in the trim and fill analysis. All of them show increases in accident numbers on roads with road lighting and have relatively small weights. The summary effect that is based on all, original and simulated, results is consequently somewhat less favourable (15% accidents on lit roads compared with unlit roads) than the summary effect that is based on the original estimates of effect only (18% accidents on lit roads compared with unlit roads). The difference is, however, not large. Other biases can produce results that resemble those expected when there is publication bias. The most frequent such bias occurs when results that refer to different accident severities are combined in one analysis. Many road safety measures have larger effects on more severe accidents. One such measure is road lighting; other measures are guardrails, roundabouts, electronic stability control, seat belts and numerous others. Evaluation studies of such measures are likely to find larger effects on fatal accidents
Part I: 2. Literature Survey and Meta-Analysis
29
700
Statistical weight (fixed effects)
600 500
Summary effect (fixed effects): -0.194 Summary effect (FE) with trim and fill: -0.158 original results Results simulated with trim and fill
400 300 200 100 0 2.0
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
-2.0
-2.5
-3.0
Effect estimate (natural logarithm; 0 = no effect)
Figure 2.2: Funnel graph of studies that have evaluated the effects of road lighting on the number of accidents (unspecified severity), results from trim and fill analysis. than on injury accidents. Since there are usually far more injury accidents than fatal accidents, the (large) estimates of effect that refer to fatal accidents will have smaller statistical weights than the (smaller) estimates of effect that refer to injury accidents. A distribution of estimates of effect that refer to a mixture of fatal and injury accidents may therefore tempt one to conclude that there is publication bias. This is illustrated in Figure 2.3, which consists of the same data points as Figure 2.1. The results that refer to fatal accidents (black) have small statistical weights and on the average are more on the right side of the distribution, that is, show larger accident reductions than the results that refer to non-fatal accidents (white). The trim and fill analysis that has been applied to the data in Figure 2.2 should therefore not have been applied to these data. The skewness of the distribution is more likely to be due to the mixing of accident severities, and not due to the publication bias. In this case, there is an apples and oranges problem and a summary effect should not be calculated based on all results. Sensitivity to outliers denotes how strongly the mean in a distribution is affected by one single atypical result (outlier). If a single, outlying result decisively influences the weighted mean, this will hardly be representative of the majority of the results.
30
The Handbook of Road Safety Measures
700
Statistical weight (fixed effects)
600 500 400
Non-fatal Fatal
300 200 100 0 2.0
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
-2.0
-2.5
-3.0
Effect estimate (natural logarithm; 0 = no effect)
Figure 2.3: Funnel graph of studies that have evaluated the effects of road lighting on the number of accidents (unspecified severity), results for fatal and non-fatal accidents. Sensitivity to outliers can be tested by calculating the g1 average, where one result after another is excluded and comparing these averages with the average of all g results. If no differences are found, then the weighted mean result is robust against outliers. When outliers are found, it may be investigated if there are characteristics of the respective study that can explain the result. However, there are no (objective) criteria for what results can be regarded as outliers. Whether a result is regarded as an outlier and the explanations that may be found are to a large degree subjective and may be arbitrary. Moreover, whether the result is affected by individual estimates of effect is highly dependent on the number of available estimates of effect. When there are only few, almost any effect estimate may be regarded as an outlier, whereas almost no estimates of effect will seem to be outliers when there are a large number of estimates of effect. Therefore, outliers are normally not omitted from the analysis.
2.6 DEVELOPING
ACCIDENT MODIFICATION FUNCTIONS
It is increasingly recognised that the effects of road safety measures cannot always be adequately described in terms of a single point estimate, such as a given percentage
Part I: 2. Literature Survey and Meta-Analysis
31
change in the expected number of accidents. The effects of many road safety measures are likely to vary, depending on characteristics of the measure and of the context into which it is introduced. To describe systematic variation in the effects of road safety measures, accident modification functions need to be developed (Elvik 2009). Figure 2.4 gives an example of an effect modification function (Elvik 2009). It is based on studies in Norway, Sweden and Denmark that evaluated the effects on road safety of constructing bypass roads, which lead to long-distance traffic outside towns. In Figure 2.4, the percentage change in the number of injury accidents is given as a function of the number of inhabitants in the bypassed town. The function in Figure 2.4 has been fitted to 10 data points. Each data point is shown by a dot, surrounded by the 95% confidence interval for the data point. As can be seen from the function fitted to the data points, the effects of bypass roads are largest in small towns and decline as the size of the town increases. An accident modification factor of 0.80 corresponds to an accident reduction of 20%. An accident modification factor of 1.00 suggests that the number of accidents did not change.
Accident modification factor
1.400 y = 0.3878x0.0991 R2 = 0.3632
1.200 1.000 0.800 0.600 0.400 0.200 0.000 0
2000
4000
6000
8000
10000
12000
14000
16000
Population
Figure 2.4: Accident modification function for bypasses. Effect on accidents as a function of the size of the population in the bypassed town.
32
The Handbook of Road Safety Measures
2.7 SPECIFICATION
OF ACCIDENT OR INJURY SEVERITY
Results that refer to accidents at different levels of severity have for the most part been kept apart. On a few occasions, it has been impossible to avoid mixing results that refer to different levels of severity because the studies we refer to have combined accidents and injuries with different levels of severity. For measures that primarily affect the number of accidents, a distinction is made between the following levels of accident severity:
Fatal accidents, which normally refer to accidents where at least one person is killed immediately or dies within 30 days of the accident Injury accidents; in the majority of cases, these also include fatal accidents Property damage only accidents Accidents of unspecified severity, which in a majority of cases will include a mixture of fatal, injury and property damage only accidents, in unknown proportions.
For measures that primarily affect injury severity, a distinction is often made between the following levels of severity:
Fatal injury Serious injury (mostly injuries that require hospitalisation) Slight injury (mostly injuries that require medical treatment, but not hospitalization) No injury.
No standard definition of injury severity is used in road safety evaluation studies. The above definitions represent those that are found most often in evaluation studies. Injury severity is often classified by means of the Abbreviated Injury Scale (AIS scale), the Maximum Abbreviated Injury Scale (MAIS) or the Injury Severity Score (ISS). AIS has six values: AIS 6 is killed, AIS 3, 4 and 5 are seriously injured, AIS 2 is moderately injured and AIS 1 is slightly injured. Each of six regions of the body is assigned one value that refers to the most serious injury to the respective body region. MAIS is based on the AIS. The MAIS value of an injured person is the highest AIS value that person has on any body region. ISS is also based on the AIS. The human body is divided into six regions, and each of these regions is assigned an AIS score. ISS is calculated as the sum of the squared values of the three body regions with the highest AIS scores.
Part I: 2. Literature Survey and Meta-Analysis
2.8 UPDATED
ESTIMATES OF EFFECT:
REVISION
33
OF THE BOOK
The Norwegian edition of this book was finished in late 1997. By 2009, Part I and over half of all chapters in Part II have been revised. The dates when each of the chapters in Part II has been last revised are stated in the introductory sections of the chapters. Readers may wonder why we publish a book in which part of the material is by now up to 10 years old. The answer is that this is a choice we had to make in view of the amount of resources we have available for this work. Revising the whole book is a major research project, which would have taken a number of years and further delayed publication of the English edition. Therefore, the chapters that were chosen for revision since 1997 are those where most recent accident studies were available. As research is going on, the revision of this handbook will never be finished.
3.
FACTORS CONTRIBUTING 3.1 A
TO
ROAD ACCIDENTS
SIMPLE CONCEPTUAL FRAMEWORK
The number of people killed or injured in road accidents depends basically on the three factors: exposure, accident rate and injury severity (Nilsson 2002). Exposure denotes the amount of activity in which accidents may occur. Any human activity is exposed to the risk of accident, but as far as road traffic is concerned, the amount of activity usually refers to the amount of travel, that is the number of person kilometres of travel performed. Despite the apparent simplicity of this definition (the amount of travel), measuring exposure in a theoretically satisfactory way is difficult. Some of these difficulties are discussed in Section 3.4. There are various ways in which one can travel by road: as a pedestrian, by cycling, by driving a car, by taking the bus, etc. Not all of these ways involve the same level of accident risk. The type of exposure chosen is therefore an aspect of exposure that influences the number of people killed or injured in road accidents. Furthermore, the risk to which one is exposed as a road user is probably not independent of the combination of various means of transport in traffic. The risk of accident to each pedestrian, for example, is usually lower, the higher the proportion of pedestrians in traffic. Hence, the relative proportions of the various means of transport in a traffic stream may influence the overall level of risk represented by that traffic stream. Accident rate is the risk of accident per unit of exposure and is an indicator of the probability of accident occurrence. Although an accident rate is not identical to an estimate of probability, it is a useful indicator as the probability of accident occurrence,
The Handbook of Road Safety Measures Copyright r 2009 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISBN: 978-1-84855-250-0
36
The Handbook of Road Safety Measures
in the theoretical sense, can be assumed to be proportional to the accident rate. The higher the accident rate, the higher the probability of an accident on a given trip of a given length. Sometimes the terms risk of accident, level of risk, or accident risk will be used synonymously with accident rate. Although the accident rate is defined per unit of exposure, it is not independent of exposure. Ideally, an exposure measure ought to be defined in a way that accident rate and exposure are independent. Such an exposure measure is, however, not available to date. The probability of accident occurrence is affected by a very large number of risk factors related to the elements of the traffic system: infrastructure and traffic control devices, vehicles and road users. A risk factor for accidents is any factor that increases the probability of accident occurrence. Risk factors are, in other words, statistically related to the probability of accidents, but not all risk factors can be regarded as causes of accidents in a stricter sense of the term. Injury severity refers to the outcome of accidents in terms of injuries to people or damage to property. The severity of the consequences of an accident is, in theory, a continuous variable ranging from the smallest fender bender to disasters with multiple fatalities. In practice, simple scales that take on just a few discrete values are often constructed to indicate accident or injury severity. As an example, official road accident statistics in most countries classify accidents by severity according to the following simple scale: fatal accident, accident resulting in serious injury, accident resulting in slight injury and property damage accidents. These crude categories are not comparable even between countries. The outcome of an accident in terms of injury to people or damage to property is also affected by a very large number of factors, related to the same elements of the traffic system as the risk factors for accidents. In this book, no attempt will be made to survey everything that is known about risk factors contributing to road accidents. An exhaustive review of this huge topic would easily fill the pages of a large book. The review is therefore limited to some of the factors that have been identified as important. A simple taxonomy of the main factors that influence road safety is presented in Figure 3.1. In principle, there are four ways of reducing the number of persons killed or injured in road accidents:
By reducing exposure to the risk of accident, that is, by reducing the amount of travel By shifting travel to means of transport that have a lower level of risk
Part I: 3. Factors Contributing to Road Accidents
Amount
Type / mode
37
Mixture
Exposure Road injuries Injury severity Infrastructure
Accident rate Vehicles
Road users
Figure 3.1: A taxonomy of factors affecting road safety.
By reducing the accident rate for a given amount of travel By reducing accident severity, that is, by protecting people better from injury
This chapter gives a brief overview of the scope of the road accident problem worldwide, as well as of the most important factors that contribute to road accidents. First, a survey of the number of road accident fatalities worldwide is presented.
3.2 THE
SCOPE OF THE ROAD ACCIDENT PROBLEM WORLDWIDE
There is no statistics showing the total number of people killed or injured in road accidents in the world. According to the World Health Organization (2008), nearly 1.2 million people are killed in road accidents annually, but no reliable estimate of the number of people injured worldwide in road accidents can be given. Rules for the reporting of road accidents and actual levels in reporting are incomparable and incomplete. As shown in Section 3.3, reporting in official statistics of road accidents leading to injury is very incomplete even in the most highly developed countries of the world with a significant number of motorised vehicles. In many highly motorised countries belonging to the Organization for Economic Cooperation and Development (OECD), the number of road accident fatalities has been reduced after a peak was reached in about 1970. The current number of road accident fatalities in some of the OECD countries is less than 50% of its peak value. Against this background, some people might think that road accidents are gradually becoming a less serious problem in the world as a whole. This impression is not correct. The number of road accident fatalities in the world as a whole continues to grow every year. In the Global Burden of Disease study performed by the World Health Organization (Murray and Lopez 1996), it was concluded that, worldwide, traffic fatalities are
38
The Handbook of Road Safety Measures
becoming more important as a cause of death. This study predicted that road accidents would become the sixth largest cause of death in the world in 2020, whereas it was the ninth largest cause of death in 1990. The number of disability-adjusted life years caused by road accidents was predicted to increase from ninth largest in 1990 to third largest in 2020. A disability-adjusted life year is a measure of how the quality of life is affected by disease or injury, on a scale ranging from 0 (no disability) to 1 (dead). Probably the best international database on road accidents is the International Road and Traffic Database (IRTAD) that operates within the framework of the Joint OECD/ECMT Transport Research Centre, and gathers data on road accidents from 37 countries, 28 of which are the members of the OECD (no data are available from Turkey and Mexico, which also are OECD members). Key figures from this database can be accessed from the homepage of the http://internationaltransportforum.org/ irtad/index.html. An advantage of the IRTAD database is that it applies the 30-day definition of a traffic fatality for all member countries. This definition states that a person is counted as a traffic accident fatality if he or she dies within 30 days after the accident. Table 3.1 presents the most recent figures concerning traffic fatalities in the countries that are members of IRTAD. Table 3.1 also shows two measures of road accident fatality risk that are labelled ‘health risk’ and ‘traffic risk’. Health risk is the number of road accident fatalities per year per 100,000 inhabitants. Health risk attributable to road accidents can be compared with the health risk represented by other causes of death, as was done in the Global Burden of Disease Study. The size of the health risk posed by road accidents in a country depends on three main factors: (1) the amount of travel per year per inhabitant in a country, (2) the level of traffic risk and (3) the resources available to protect road users from fatal injury or provide rapid medical treatment for serious injuries, to prevent them from becoming fatal. Traffic risk is the number of road accident fatalities per year per 1 billion motor vehicle kilometres of travel. The level of traffic risk indicates how safe road travel is in a country. Table 3.1 shows that there are fairly large variations in both health risk and traffic risk between the IRTAD countries. All these countries are highly motorised, having at least 0.14 motor vehicles per inhabitant in 2000. As far as road accident fatalities in other countries of the world are concerned, the available statistics are less comprehensive. The most inclusive statistics are the World Road Statistics, collected by the International Road Federation. These statistics can be accessed via the website of the International Road Federation (http://www.irfnet.org).
Part I: 3. Factors Contributing to Road Accidents
39
Table 3.1: Road accident fatalities, health risk and traffic risk in IRTAD member countries (IRTAD) Motor vehicles per inhabitant 2006
Road accident fatalities 1980
2006
Traffic risk2
2006
2006
Australia
0.68
3,272
Austria
0.64
2,003
730
8.8
8.9
Belgium
0.59
2,396
1,069
10.2
11.1
1,043
13.5
0.62
5,461
Bulgaria Canada Cyprus
1,598
Health risk1
8.0
2,925
9.1
86
11.2
9.2
Czech Republic
0.48
1,261
1,063
10.4
Denmark
0.45
690
306
5.6
204
15.2
Finland
0.57
551
336
6.4
6.4
France
0.61
13,499
4,709
7.5
8.5
Germany
0.67
15,050
5,091
6.2
7.4
Greece
0.63
1,446
1,657
14.9
Hungary
0.34
1,630
1,305
13.0
Iceland
0.79
25
31
10.3
10.9
Ireland
0.50
564
368
8.7
10.9
Israel
0.31
434
414
5.9
9.6
Italy
0.75
9,220
669
9.6
Japan
0.65
11,388
7,272
4.7
407
17.7
759
22.3
36
7.7
10
2.5
Estonia
Latvia Lithuania Luxembourg
0.82
98
Malta
20.5
The Netherlands
0.53
1,996
730
4.5
7.7
New Zealand
0.75
597
391
9.3
10.1
Norway
0.66
362
242
5.2
6.5
Poland
0.47
6,002
5,243
13.7
Portugal
0.52
2,579
Romania Slowakia
969
9.2
2,478
11.5
579
10.7
Slovenia
0.58
558
262
13.1
16.5
South Korea
0.38
6,449
6,376
13.0
19.3
Spain
0.65
6,522
4,104
9.4
Sweden
0.58
445
4.9
5.9
40
The Handbook of Road Safety Measures
Table 3.1: (Continued ) Motor vehicles per inhabitant 2006
Road accident fatalities 1980
2006
Health risk1
Traffic risk2
2006
2006 5.9
Switzerland
0.68
848
370
5.0
UK
0.57
6,182
3,297
5.5
USA
0.83
51,091
43,443
14.7
9.0
Total IRTAD
0.67
152,174
101,017
10.1
9.3
Total IRTAD (1980 available): 1 2
95,006
Killed per 100,000 inhabitants. Killed per 1 billion vehicle km.
It includes more than 185 countries. Key figures from these countries, including the IRTAD member countries, are presented in Table 3.2a (all countries) and Table 3.2b (summarised for continents/regions). These statistics are by no means complete. They are limited to those countries that had reported traffic fatalities, the number of motor vehicles and the size of the population to the International Road Federation for the years 2001–06. One should also keep in mind that different countries may apply different definitions of a traffic fatality – not all countries use the 30-day definition – and that the reporting of road accident fatalities is not complete in all countries. Moreover, it is not known to what degree the numbers of registered motor vehicles correspond to the numbers of motor vehicles actually in use. As a whole, Australia/New Zealand and Europe have the most favourable road safety records, with traffic risk being lower than in other parts of the world and health risk among the lowest. North, Central and South America and the former East Bloc countries have somewhat higher traffic risk. In South and Central America, the low health risk is related to the fact that the motorisation rate is quite low. However, the standard deviations of both health and traffic risk are high compared with the average risks. The countries of the former East Bloc, more than half of which are members of the IRTAD, are performing better than the former USSR countries on both safety indicators. Other parts of the world have poorer road safety records, especially Africa that has the highest traffic risk. In Asia and Africa, the health risk is about the same as that in Europe, while traffic risk is about 10 times as high or higher. The large standard deviations in the traffic risk are mainly due to some countries with extremely high health risk (over 1,000 fatalities per 100,000 motor vehicles). These countries are Ethiopia, Uganda, Bangladesh and
Part I: 3. Factors Contributing to Road Accidents
41
Table 3.2a: Road accident fatalities, health risk and traffic risk for selected countries (International Road Federation, World Road Statistics, 2008)
Country
Year
Number of road Health risk (fatalities accident per 100,000 fatalities inhabitants)
Traffic risk (fatalities per 100,000 motor vehicles)
Africa Algeria
2005/2003
4,000
12.6
Angola
2002/2001
942
6.6
134.5
Botswana
2005/2004
532
30.1
267.2
Brunei Darussalam
2000/2006
32
8.4
24.7
Congo, Rep.
2002/2004
126
3.2
Ethiopia
2003/2002
1,628
2.4
Gabon
2000
293
23.0
Ghana
2006/2001
1,242
6.1
302.0
Kenya
2004
2,264
6.8
372.9
Lesotho
2000
290
16.4
Malawi
2005
903
7.0
Mauritania
2006
186
6.1
Mauritius
2006
134
10.7
74.8
Morocco
2003/2006
3,754
12.3
220.7
Namibia
2002
340
17.4
203.6
Niger
2005/2006
371
2.7
487.8
Nigeria
2004
5,351
4.2
Senegal
2000
646
6.4
473.4
Sierra Leone
2002/2003
70
1.3
365.9
South Africa
2006
15,419
32.5
215.3
Swaziland
2003/2001
255
23.9
273.9
Tunisia
2004
1,533
15.4
162.2
Uganda
2000
1,527
6.5
1,272.0
Zimbabwe
2002/2000
1,433
11.4
1,132.8
Asia Bangladesh
2006/2003
4,000
2.9
Cambodia
2000/2003
824
6.1
1,250.8 180.2
China
2006/2005
98,738
7.6
268.4
China, Hong Kong
2006/2005
151
2.2
31.1
China, Macao
2004
16
3.5
23.8
Chinese Taipei
2006
3,140
13.7
45.9
India
2003/2005
94,968
8.7
740.0
Indonesia
2002/2005
11,451
5.2
49.8
42
The Handbook of Road Safety Measures
Table 3.2a: (Continued )
Country IRTAD
Year
Number of road Health risk (fatalities per 100,000 accident inhabitants) fatalities
Traffic risk (fatalities per 100,000 motor vehicles)
Japan
2004
8,492
6.7
Korea, Republic of
2006/2005
6,376
13.2
11.3 40.1
Lao PDR
2002/2006
608
10.5
193.0
Malaysia
2003
6,282
25.7
94.4
Maldives
2004
–
Mongolia
2003/2002
415
17.0
390.5
–
Myanmar (Burma)
2006/2003
1,308
2.6
466.7
Pakistan
2004/2006
4,428
2.8
214.2
Singapore
2006
Sri Lanka
2006/2002
St Vincent & Grenadines 2003/2002
190
4.2
30.0
2,029
10.7
186.6 61.5
10
8.5
Thailand
2003
14,446
22.9
Vietnam
2002/2003
11,319
13.9
1,788.6
Australia/New Zealand IRTAD
Australia
2005
1,627
8.1
12.1
IRTAD
New Zealand
2006
393
9.4
13.0
Europe IRTAD
Austria
2006
730
8.8
15.9
IRTAD
Belgium
2006
1,069
10.2
19.0
IRTAD
Cyprus
2005
102
13.5
18.1
IRTAD
Denmark
2005/2006
306
5.6
12.9
IRTAD
Finland
2006
336
6.3
11.7
IRTAD
France
2006
4,708
7.7
12.8
IRTAD
Germany
2006
5,091
6.2
10.3
IRTAD
Greece
2005/2006
1,657
14.9
30.0
IRTAD
Iceland
2005/2006
31
10.3
14.4
IRTAD
Ireland
2003/2004
379
9.3
21.2
IRTAD
Italy
2005/2006
5,669
9.6
14.5
IRTAD
Luxembourg
2004
50
11.0
15.1
IRTAD
Malta
2004
13
3.2
5.1
IRTAD
The Netherlands
2002/2006
730
4.5
9.3
IRTAD
Norway
2005/2004
259
5.6
10.3
IRTAD
Poland
2005/2006
5,243
13.8
35.6
IRTAD
Portugal
2003/2006
969
9.2
18.3
IRTAD
Spain
2003/2004
4,741
11.1
20.5
Part I: 3. Factors Contributing to Road Accidents
43
Table 3.2a: (Continued )
Country
Year
Number of road Health risk (fatalities per 100,000 accident inhabitants) fatalities
Traffic risk (fatalities per 100,000 motor vehicles)
IRTAD
Sweden
2006
445
4.9
IRTAD
Switzerland
2006
370
4.9
8.7
Turkey
2005/2006
4,633
6.4
55.0
United Kingdom
2005/2006
3,172
5.2
10.2
89.7
IRTAD
9.5
Former East Bloc Albania
2006
277
8.7
Bosnia and Herzegovina
2004
251
6.4
Bulgaria
2004/2006
1,043
13.6
37.4
Croatia
2006
614
14.0
38.1
IRTAD
Czech Republic
2002/2006
1,063
10.3
26.5
IRTAD
Hungary
2003/2006
1,303
12.9
40.7
Macedonia, FYR
2002/2004
155
7.6
46.9
Moldova
2005/2006
382
10.1
96.8
Romania
2005/2006
2,478
11.5
63.6
Serbia
2006
900
12.2
49.9
IRTAD
Slovakia
2006
608
11.3
39.2
IRTAD
Slovenia
2006
262
13.1
24.7
IRTAD
IRTAD
Former USSR
IRTAD
Armenia
2006
332
11.1
Azerbaijan
2005/2006
1,027
12.1
Belarus
2006
1,726
17.8
199.4
Estonia
2005/2006
204
15.2
31.8
Georgia
2003/2006
675
15.3
208.5
Kazakhstan
2006
4,271
27.9
200.3
Kyrgyzstan
2005
893
17.4
IRTAD
Latvia
2006
407
17.7
IRTAD
Lithuania
2006
759
22.3
43.5
Russian Federation
2006
32,700
23.0
100.5
Tajikistan
2000/2004
415
6.5
Turkmenistan
2002
519
11.2
Ukraine
2005/2006
7,592
16.2
125.9
30.0
42.7
Middle East Bahrain
2003/2002
81
11.6
Egypt
2005/2006
6,000
8.1
Iran
2002/2006
8,257
11.8
44
The Handbook of Road Safety Measures
Table 3.2a: (Continued )
Country IRTAD
Year
Number of road Health risk (fatalities per 100,000 accident inhabitants) fatalities
Traffic risk (fatalities per 100,000 motor vehicles)
Israel
2005/2006
414
5.9
20.4
Jordan
2006
899
16.3
128.6
Kuwait
2004
400
16.3
38.5
Lebanon
2000/2006
375
9.3
Oman
2002/2003
578
23.0
Palestine
2005
159
4.4
Qatar
2002/2006
270
32.9
77.4
Saudi Arabia
2005
5,982
25.3
134.5
Syria
2006
2,197
11.3
245.1
United Arab Emirates
2001/2003
873
21.6
120.5
North America IRTAD
Canada
2003/2004
IRTAD
United States
2005
2,730
8.5
14.8
43,443
14.7
21.7
South/Central America Anguilla
2004
Bahamas
2002/2005
Barbados Bolivia
1
15.0
68
21.1
2004/2002
24
9.0
23.8
2004/2001
823
9.7
187.2 19.6
Brazil
2004
6,119
3.3
Chile
2006/2001
1,562
10.0
60.5
Colombia
2005/2006
5,486
12.0
204.2
Costa Rica
2004/2006
Ecuador
2006
Jamaica
2006/2005
Mexico
2006
329
7.5
39.1
1,801
13.6
205.5
326
12.3
64.2
4,908
4.7
21.2
Nicaragua
2004/2002
473
9.2
190.9
Panama
2002
401
13.1
127.3
Paraguay
2000
160
3.0
35.4
Peru
2004/2006
3,481
12.6
270.4
Philippines
2005/2006
961
1.1
34.4
Puerto Rico
2001
496
12.9
Suriname
2004
69
15.5
64.8
Uruguay
2005
150
4.3
24.7
Part I: 3. Factors Contributing to Road Accidents
45
Table 3.2b: Road accident fatalities, health risk and traffic risk summarised for several continents/regions (International Road Federation, World Road Statistics, 2008) Number of countries
Number of motor vehicles per 100 population
Health risk (SD)
Traffic risk (SD)
2
68
2,020
8.3 (0.9)
12.2 (0.6)
22
49
40,703
8.0 (3.3)
16.2 (11)
2
67
46,173
14.1 (4.3)
21.1 (4.9)
South/Central America
18
13
27,638
5.4 (5.1)
39.7 (84.6)
Former East Bloc
12
27
9,336
11.4 (2.4)
43.3 (23.8)
Middle East
13
16
26,485
12.2 (8.4)
105.5 (74.7)
Former USSR
13
20
51,520
20.3 (5.7)
106.1 (76.2)
Asia
20
6
269,191
8.0 (6.7)
138.9 (463.7)
Africa
24
5
43,271
9.0 (8.6)
224.0 (348.6)
Australia/New Zealand Europe North America
Number of road accident fatalities
Traffic risk (fatalities per 100,000 motor vehicles)
1,800 Europe Australia, New Zealand North America
1,600 1,400
South/Central America Former East Bloc Middle East Former USSR
1,200 1,000
Asia Africa
800 600 400 200 0
5
10
15
20
25
30
35
Health risk (fatalities per 100,000 population)
Figure 3.2: Relationship between health risk and traffic risk in different countries (International Road Federation, World Road Statistics, 2008). Vietnam; India has 750 fatalities per 100,000 motor vehicles. It is not known to what degree the high risk can be explained with unregistered motor vehicles. Figure 3.2 shows the relationship between health risk and traffic risk in all countries listed in Tables 3.1, 3.2a and 3.2b. It appears that there is a negative relationship
46
The Handbook of Road Safety Measures
between health risk and traffic risk in Asia and Africa, that is a high health risk is related to a low traffic risk and vice versa. In other countries, there is no such relationship. In the North American European and North American countries, Australia and New Zealand, there is a slight tendency for higher health risk to be associated with higher traffic risk. Many years ago, Smeed (1949) postulated a law-like relationship between health risk and motorisation rate, stating essentially that as the motorisation rate goes up, the number of road accident fatalities per inhabitant goes down. The data presented in Tables 3.1, 3.2a and 3.2b do not lend support to the idea of such a relationship. The relationship between motorisation (motor vehicles per 100 inhabitants) and health risk is shown in Figure 3.3 for countries that are included in Table 3.1, 3.2a and 3.2b.
Health risk (fatalities per 100,000 population)
Figure 3.4 shows the relationship between motorisation (vehicles per 100 inhabitants) and traffic risk (fatalities per 100,000 vehicles). There are only five countries with more than 500 fatalities per 100,000 vehicles, which are not shown in Figure 3.3. The motorisation rate in these countries is very low (between 0.2 and 1.2 motor vehicles per 100 inhabitants). The data suggest that traffic risk decreases as motorisation increases. For the countries with a low motorisation rate, the spread of the data points seems to be particularly great.
35
Europe Australia, New Zealand North America
30 25
South/Central America Former East Bloc Middle East Former USSR
20
Asia Africa
15 10 5 0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Motor vehicles per 100 population
Figure 3.3: Relationship between motorisation and health risk.
0.8
Traffic risk (fatalities per 100,000 vehicles)
Part I: 3. Factors Contributing to Road Accidents
47
500 Europe Australia, New Zealand North America
400
South/Central America Former East Bloc Middle East Former USSR Asia Africa
300
200
100
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Motor vehicles per 100 population
Figure 3.4: Relationship between motorisation and traffic risk.
3.3 INCOMPLETE
REPORTING IN OFFICIAL ROAD ACCIDENT STATISTICS
It is well known that the reporting of road accidents in official statistics is incomplete and biased. Incomplete road accident reporting is part of a larger problem concerning the availability of accurate information about road accidents. Figure 3.5 traces the sources of error and loss of data in official accident records. Starting with all accidents that actually occur on public roads, the first loss of information occurs because some of these accidents are not defined as reportable to the police. In Norway, for example, accidents that are not reportable include all accidents involving pedestrians only and all accidents in which inconsequential personal injuries are sustained. It is known from a large number of studies, summarised by Elvik and Mysen (1999), that the reporting of reportable injury accidents in official statistics is very incomplete. A large number of potentially important data elements, related to human factors in particular (Elvik and Vaa 1990), are not recorded. Finally, there are errors or missing information in some of the recorded data elements. In Norway, for example, information about the use of seat belts is missing for about 55% of car drivers who are killed. Virtually all studies of the effects of road safety measures are based exclusively on official accident statistics. However, very few studies seem to have probed the implications of these, more or less inevitable, errors. Hauer and Hakkert (1988) have
48
The Handbook of Road Safety Measures
Stages of accident recording
Lost or inaccurate information
All accidents on public roads Accidents defined as reportable
Not reportable accidents
Accidents reported
Incomplete reporting
Data elements recorded
Missing data elements
Accuracy of recorded data
Inaccurate data
Figure 3.5: Sources of error and data loss in official accident records. examined some of the implications of incomplete accident reporting. One of the most important implications is that incomplete accident reporting increases the uncertainty of the estimated effects of road safety measures. Elvik and Mysen (1999) have conducted a meta-analysis of studies of accident reporting in official statistics reported in 13 countries. The study was based on earlier reviews made by Borger (1995), Hauer and Hakkert (1988), Hvoslef (1994) and James (1991), but included more studies than any of those reviews. The definition of the level of accident reporting. Most studies of the level of accident reporting in official statistics involve comparing accidents and injuries recorded at hospitals with those recorded by the police in the area serviced by the hospital or hospitals. There are two reasons why such a comparison is unlikely to show the true reporting level for injury accidents in official road accident statistics. First, at least three definitions of reporting level can be derived from a comparison of police records and hospital records (Figure 3.6). B BþC AþB Definition 2 ¼ BþC AþB Definition 3 ¼ AþBþC
Definition 1 ¼
The simplest definition, and the one most commonly used in studies that rely on hospital records, is the proportion of cases recorded by the hospital for which a police report is found. Another common definition of the reporting level is simply to count the total number of cases recorded by the police as a proportion of the total number of cases recorded by the hospital. The theoretically most correct definition counts police
Part I: 3. Factors Contributing to Road Accidents
Accidents recorded by hospitals
A: Accidents recorded by the police ONLY
A
B
C
B: Accidents recorded by the police AND hospitals
49
Accidents recorded by the police
C: Accidents recorded by hospitals ONLY
Figure 3.6: Illustration of three definitions of the level of accident reporting. reported cases as a proportion of cases recorded by A – the police alone, B – both the hospital and the police and C – the hospitals alone. The choice of definition can make a big difference for the estimated level of reporting. In Figure 3.6, if A is 100, B is 200 and C is 400, definition 1 gives a reporting level of 33% (200/600), definition 2 a reporting level of 50% (300/600) and definition 3 a reporting level of 43% (300/700). Unfortunately, not all studies state precisely how reporting level was defined. The use of definition 3 requires a study of individual records to find the number of cases that belong to each of the categories A, B and C. In studies that simply compare the total number of cases recorded by the police and the hospital, the reporting level is defined according to either definition 1 or definition 2 in Figure 3.6. Neither of these definitions is theoretically correct. The second reason why a definition of reporting level based on a comparison of police and hospital records is unlikely to show the true reporting level for injury accidents is that hospital records are unlikely to be complete. There are a number of sources of road accident data, including police records, hospital records, insurance records, other company records and self-reported accidents. It is widely agreed that none of these sources is likely to be complete. In order to get the most complete coverage of accidents, ideally data from all these sources should be combined and their degree of overlap should be determined exactly; however, this has never been done. Virtually all studies of road accident reporting compare just two sources of data, the most common comparison being between police records and hospital records. It follows that no exact determination of the level of reporting for road accidents is possible. The true number of accidents is unknown and is not recorded anywhere. Any estimate based on available sources is likely to show the lower bound for the true number of road accidents.
50
The Handbook of Road Safety Measures
Meta-analysis of studies of accident reporting. Elvik and Mysen (1999) made a metaanalysis of 49 studies that have evaluated the reporting level for road accidents involving personal injury. The studies included are listed in the paper by Elvik and Mysen (1999). Reporting level was defined as the number of police reported cases divided by the number of cases in the main source of data. No distinction was made between the various definitions of reporting level discussed above because not all studies state exactly how the reporting level was defined. Reporting levels by injury severity. The reporting level for injury accidents in official road accident statistics depends on injury severity. Injury severity is, however, a variable that is likely to be defined in different ways in different countries. In the metaanalysis of accident reporting, a crude distinction has been made between the following levels of injury severity:
Fatal injuries: death within 30 days after the accident Serious injuries: injuries that result in admission to hospital as an in-patient Slight injuries: injuries treated at hospitals, but not resulting in admission as an inpatient Very slight injuries: injuries treated medically outside hospitals Property damage only, accidents in which nobody was injured. In many countries, property damage only accidents do not have to be reported.
Figure 3.7 shows the mean reporting level for injury accidents according to these levels. The mean reporting level given for each level of injury severity is a weighted average based on studies reported in several countries. The vertical bars indicate the range of reporting levels found in the studies. Reporting levels by groups of road users. Table 3.3 shows the mean reporting level for injury accidents in various countries for various groups of road users and types of accident. Table 3.3 shows a remarkably consistent pattern in accident reporting in various countries. In general, the reporting level is highest for car occupants. It is generally slightly lower for pedestrians, still lower for motorcycle riders and lowest for cyclists. With the exception of Great Britain, the reporting of cyclist injuries is roughly between 10% and 25%. For single vehicle accidents among cyclists, reporting is very low, below 10% in all the countries that have evaluated the reporting level for single vehicle bicycle accidents. Despite this consistency, the overall level of accident reporting differs greatly between countries. To compare the level of road safety between countries, one must therefore
Part I: 3. Factors Contributing to Road Accidents
51
Mean level of reporting by injury severity Percent reported in official road accident statistics
120 100 95 80 69
60 40
27
20
25 11
0 Killed
Serious injury
Slight injury
Minor injury
Property damage only
Injury severity
Figure 3.7: Mean level of accident reporting by injury severity (Elvik and Mysen 1999).
Table 3.3: Percentage of injury accidents reported by road user group and type of accident Car occupants Country
All Drivers Passengers
All 53
Collisions
Single
All Collisions Single
All
53
16
Australia
73 48
31
France
63
45
11
52
Great Britain
68
The Netherlands
63
Norway
56
Reunion
45
Sri Lanka
78
Sweden
77
Switzerland
44
USA
65
67 52
66
Pedestrians
Denmark Germany
79
Cyclists
Motor cycle riders
67 45
7
44
22
44
66
56
24
37
16
35
16
81 80
76
55 22
82
40
10
(not available)
69 31
3
45 85
3
25
29
46
2
45 46 75
59
8
51
0
8 26
81 49
26 67
39 83
70 38 56
52
The Handbook of Road Safety Measures
Table 3.4: Reporting of road accident fatalities and injuries in different countries (Hutchinson 1984) Fatalities
Injuries
The Netherlands
106
43
Germany
104
39
Denmark
97
21
Finland
96
Canada
95
88
USA
95
49
Belgium
93
Sweden
93
Australia
92
64
UK
90
57
Norway
87
37
55
France
54
Switzerland
25
rely on fatalities only. The officially recorded numbers of injured road users are too greatly affected by varying levels of reporting to be directly comparable. The reporting of road accident fatalities and injuries in different countries. Hutchinson (1984) compared the number of fatalities and injuries reported in official road accident statistics with the number reported in official mortality statistics (Table 3.4). The results that refer to fatalities are based on death certificates for a large number of countries. In all cases, the 30-day definition of a road accident fatality is applied. The results that refer to injuries are estimated as weighted mean of the results from several studies. In most countries, the reporting of fatalities in official road accident statistics is incomplete. In two countries, there is apparently a reporting of more than 100% of traffic accident fatalities. Hutchinson remarks that the reasons for this are unknown. Possible explanations for the apparent over-reporting are that persons who committed suicide, who died immediately before the accident of an acute illness or who are foreigners are assigned a different cause of death in the official death records, but retained as traffic fatalities in the road accident statistics. On average, the reporting level for traffic accident fatalities for the 11 countries included in Table 3.4 is about 95%. The reporting level for fatalities differs significantly between countries (w2 ¼ 69:554; df ¼ 10, po0.001). For injuries, the reporting level
Part I: 3. Factors Contributing to Road Accidents
53
ranges from 88% to 21%. The (weighted) mean reporting level is 39%. The differences in reporting level between the countries are very highly significant (X 2hom ¼ 43; 117:324; df ¼ 12, po0.001). A closer look at some of the studies shows that few differences between countries in reporting level are probably attributable to differences with respect to the severity of the injuries that are included. The studies in Canada were based on data dominated by serious injuries, that is, by injuries that resulted in admission to hospital as an inpatient. This clearly illustrates how difficult it is to obtain comparable accident data for different countries.
3.4 EXPOSURE: TRAFFIC
VOLUME
Ideally speaking, a study of the relationship between traffic volume and accidents ought to control for the effects of all other factors that affect accident occurrence. ‘To control for’ means to remove, so that the effects of the amount of travel are not mixed up with the effects of other factors. Such other factors are, for example, road category (lower accident rate on better roads) and time of the year or day (higher accident rates in darkness). Studies of the relationship between exposure and accidents usually refer to traffic volume, not the amount of travel, that is normally defined as the number of motor vehicles using a road per unit of time. Pedestrians and cyclists tend not to be included, usually because there are no reliable counts of their numbers. The volume of travel includes passengers in addition to drivers. The relationship between exposure and accidents is reviewed in Chapter 10.5; here only a summary is given. The effects of the amount of travel on the number of accidents can be expressed in many ways. Two of the most informative are to
describe, by means of a mathematical function, the shape of the relationship between traffic volume and accidents, and indicate the contribution that traffic volume makes to explaining systematic variation of the number of accidents.
The relationship between traffic volume and accidents. Increasing traffic volumes are usually related to increasing numbers of accidents. However, the number of accidents is not linearly related to traffic volume. Usually, the percentage increase of the number of accidents is less than the percentage increase of traffic volume. Increasing traffic
54
The Handbook of Road Safety Measures
volumes are often related to better road standards, and drivers may pay more attention at high volumes than at low volumes. On the basis of a large number of studies, the estimated relationship between traffic volume and accidents is shown in Figure 3.8. Traffic volume is expressed as the annual average daily traffic (AADT), which is the most commonly used, although not necessarily the best, measure of traffic volume. The mathematical function used to describe the relationship N ¼ AADTb As volume increases by 10%, the estimated increase of the number of accidents is 8.8% with a 95% confidence interval from 7.7% to 9.9%. The uncertainty in the estimated change of the number of accidents is due to a number of factors. First, the form of the mathematical function may not be optimal. The percentage change of the number of 50 (95% confidence interval) Rel. number of accidents (1 at AADT = 1,000)
Relative number of accidents 40
30
20
10
0 0
10,000
20,000
30,000
40,000
50,000
AADT
Figure 3.8: Relationship between traffic volume (AADT) and the number of accidents, estimated by means of meta-analysis based on 28 studies.
Explanatory factor
Part I: 3. Factors Contributing to Road Accidents
Traffic volume
55
66.8
Weather and daylight
6.1
County
7.2
Month
1.5
Long term trend 0.3 Rules for accident reporting
4.8
Unexplained systematic variation Random variation
5.2 8.1 0
10
20
30
40
50
60
70
80
Percentage of explained variation
Figure 3.9: Contribution of various factors to explaining the variation in injury accidents by county and month in Norway (Fridtstrøm et al., 1993, 1995).
accidents may be different at different volumes. Second, the relationship between volume and accidents is likely to be different under different conditions, for example, depending on the type and capacity of the road, the type of accidents and variations of volume over time. The contribution of traffic volume to explaining systematic variation of the number of accidents. Figure 3.9 shows the results of a multivariate analysis of some factors that influence the number of injury accidents per county per month in Norway (Fridstrøm et al. 1993, 1995). The effects of traffic volume alone is about twice as great as the effects of all other factors combined. Some readers may raise the following objections: What about the effects of road safety measures? What about road user behaviour? Why do these factors not show up in the diagram to explain at least part of the variation of the number of accidents? The simple answer is that these factors were not included in the analysis. Even if road safety measures and road user behaviour had been included, these factors would not necessarily have explained very much of the variation in the number of accidents. To have explanatory power, these variables would have to exhibit sufficient variation in the data set. It is doubtful whether that would be the case for road safety measures. Such measures tend to be introduced gradually in small doses, so that significant variations would not be expected between counties in Norway. Despite these reservations, there is hardly any doubt that traffic volume is the single most important factor that influences the number of road accidents. This is likely
56
The Handbook of Road Safety Measures
to be the case all over the world, although of course the precise shape of the relationship between traffic volume and the number of accidents will vary from place to place.
3.5 ACCIDENT
RATES FOR DIFFERENT TYPES OF EXPOSURE
When travelling by road, the main means of transport available are walking, cycling, riding a moped or motorcycle, driving a car, being a passenger in a car and going by bus. In a number of countries, injury rates have been compared for these means of transport: Sweden: Nilsson (2002) Denmark: Brems and Munch (2008) Great Britain: UK Department for Transport (2008) The Netherlands: SWOV (2008) Norway: Bjørnskau (2008) The absolute injury rates are not comparable, due to differences in accident reporting discussed earlier. Figure 3.10 presents relative injury rates, in which the injury rate of a car driver has been set 1.0. A simple mean for the five countries has been estimated.
65.4
Moped rider 12.0
Motor cycle rider
9.4
Cyclist
6.7
Pedestrian Car passenger
1.0
Car driver
1.0
Buss passenger
0.5 0
10
20
30
40
50
60
70
Relative injury rate (car driver = 1.0)
Figure 3.10: Relative injury rates for different means of transport – mean for five countries.
Part I: 3. Factors Contributing to Road Accidents
57
A very similar pattern was found for all five countries. Travelling by moped or motorcycle involves a risk of injury, which is over 10 times higher than that of a car driver. Pedestrians and cyclists also run a high risk of being injured per kilometre of travel. Car passengers have the same risk of injury as car drivers. Travelling by bus is the safest. These estimates of relative injury rates are all based on accidents reported in official accident statistics. The differences in injury rates would probably have been even greater if these rates had been estimated on the basis of injuries recorded in hospitals. Why are pedestrians, cyclists and riders of mopeds and motorcycles at such a high risk of getting injured in road traffic? There are many reasons, and we will not go into them in detail. Referring to the conceptual model in Section 3.1, there are two classes of reasons for the high injury rate of pedestrians, cyclists and riders of mopeds and motorcycles:
factors affecting accident involvement rate and factors affecting the probability of injury, given that a road user is involved in an accident (vulnerability).
Pedestrians and cyclists tend to do most of their travel in urban areas, where the overall risk of accidents is higher than in rural areas. Moped riders are often young and inexperienced. Motorcycle riders may be more experienced; on the contrary, a motorcycle is capable of going at a higher speed than a moped. Much of the difference in injury rate between pedestrians, cyclists and rider of mopeds and motorcycles on the one hand, and car occupants on the other, is attributable to differences in the protection from injury offered in an accident. The accident involvement rates of car drivers is not very different from that of pedestrians and cyclists, but a far higher proportion of car accidents result in property damage only.
3.6 THE
MIXTURE OF ROAD USERS
Most of the road system carries mixed traffic, that is, all or most categories of road users use the same area for travel. Urban roads may have separate facilities for pedestrians and/or cyclists, but at junctions, pedestrians and cyclists normally mix with car traffic. Do the relative proportions of different road users in traffic affect the number of accidents? There is reason to believe that this is the case. Few studies have estimated the relationship between composite measures of exposure and the number of accidents or injured road users. By way of illustration, a study by Bru¨de and Larsson (1993)
58
The Handbook of Road Safety Measures
estimated the relationship between accidents involving pedestrians or cyclists and motor vehicles by means of the following composite measure of exposure: Number of accidents involving groups 1 and 2 ¼ aQb1 Qc2 In this formula, Q1 is the number of road users of type 1, Q2 is the number of road users of type 2, b and c are coefficients to be estimated and a is a scaling constant. Bru¨de and Larsson (1993) estimated the following functions: Number of pedestrian accidents ¼ 0:0000734 MV0:50 PED0:72 Number of bicycle accidents ¼ 0:0000180 MV0:52 CYC0:65 where MV is the number of motor vehicles (AADT ¼ annual average daily traffic), PED is pedestrian volume and CYC is cyclist volume. On the basis of these functions, the number of accidents can be estimated for any combination of values for the number of motor vehicles and the numbers of pedestrians or cyclists. These functions suggest a highly non-linear relationship between exposure and the number of accidents. If, as an example, the number of pedestrians increases from 500 to 1,000, and the number of motor vehicles increases from 5,000 to 10,000, the number of pedestrian accidents (i.e., accidents in which pedestrians are struck by cars) increases by a factor of nearly 2.33. In other words, the number of accidents is more than double when total traffic volume is doubled (from 5,500 to 11,000 in total). Despite this, the risk run by each pedestrian, at a given amount of motor traffic, declines strongly as the number of pedestrians increases. If the number of pedestrians increases from 100 to 1,000, the risk of getting injured, stated as the number of pedestrian accidents per pedestrian exposed, drops by about 50%. A further increase in the number of pedestrians from 1,000 to 2,000 is associated with a further reduction in the injury rate per pedestrian of some 17%. Likewise, the risk of a motor vehicle striking a pedestrian declines as a function of the number of motor vehicles interacting with pedestrians. Thus, for a given amount of pedestrian traffic, the risk of each motor vehicle hitting a pedestrian drops by more than 50% if the number of motor vehicles increases from 2,000 to 10,000. For both pedestrians and motor vehicles, a high traffic volume alone confers some protection from injury accidents. There is safety in numbers: each pedestrian is safer if more pedestrians there are. The precise behavioural mechanisms leading to this
Part I: 3. Factors Contributing to Road Accidents
59
relationship are not very well known. More dense traffic is likely to have at least two effects that are beneficial to safety: First, in dense traffic, there are more things to pay attention to. Assuming that nobody wants to be involved in an accident, drivers may strive to pay at least sufficient attention to the traffic to be reasonably sure of avoiding accidents. Moreover, driving in dense traffic is less monotonous than driving in very sparse traffic. Second, in very dense traffic, speed goes down. As speed goes down, accidents become both less likely and less severe. Pedestrians are better able to enforce their right of way when crossing the road if there are many of them than if there is just one pedestrian waiting to cross the road. Similar comments apply to accidents involving cyclists and motor vehicles. The situation almost resembles a paradox: While each road user, in each of the groups that interact, is safer in heavy traffic than in light traffic, the total number of accidents involving the interacting categories of road users increases more than in proportion to the total interacting volumes. As noted, few studies have examined the relationship between exposure and accident occurrence using composite measures of exposure. Virtually, the only applications that can be found in the literature refer to accidents at junctions, modelled as a function of intersecting traffic volumes, and studies of accidents involving pedestrians or cyclists and motor vehicles. This means that the precise nature of the relationship between exposure to risk and the number of accidents remains largely unknown at its most basic level. In most studies, only aggregate measures of exposure have been used, in most cases including motor vehicles only. Important aspects of the relationship between exposure and accidents remain hidden as long as exposure is not broken down into categories of road users that differ greatly with respect to their accident involvement rate.
3.7 A
SURVEY OF SOME RISK FACTORS FOR ACCIDENT INVOLVEMENT
A large number of risk factors have been found to be statistically associated with accident rates, that is, with the number of accidents per unit of exposure. In this brief survey, only a few of these factors will be mentioned. Type of road or traffic environment. The rate of road accidents, given as accidents per million vehicle kilometres of travel, varies greatly between different types of road and different types of traffic environment. An international comparison is given in Table 3.5 which is taken from the chapter dealing with urban and regional planning. Sources are as follows:
60
The Handbook of Road Safety Measures
Table 3.5: Relative risk on different types of roads in different countries: injury accidents (risk on motorways ¼ 1.00) Relative risk of injury accidents in different countries Type of road
Denmark
Finland
Germany
UK
Motorway
1.00
1.00
1.00
1.00
Main road
3.97
2.91
3.00
2.82
Collector road
4.67
3.27
Access road
5.67
6.11
Main road
11.00
7.86
Collector
9.11
6.82
Access road
9.98
7.35
All
4.61
3.75
Norway
The Netherlands
Sweden
USA
1.00
1.00
1.00
1.00
2.28
1.33
1.29
2.72
3.46
3.67
2.34
4.56
5.53
7.17
1.34
8.66
Rural areas
5.11
Urban areas 7.17
5.33
5.22
2.15
5.68
6.46
18.33
3.96
5.61
7.06
12.13
9.50
3.09
8.81
4.42
4.04
2.22
4.64
Source: See the text.
Denmark: Greibe and Hemdorff (2001) Finland: Tielaitos (1997) Germany: BASt (2008) UK: UK Department for Transport (2008) Norway: Erke and Elvik (2006) The Netherlands: SWOV (2008) Sweden: Thulin (1991) USA: US Department of Transportation (1991)
Relative accident rates are used because absolute rates are not comparable between different countries due to differences in accident reporting and in the estimated amounts of travel. The classification of road types in this table is rough and approximate and is only intended to demonstrate main patterns. It has not been possible to obtain accident rates for all types of roads in all countries included in the table. Thus, some cells do not show any accident rate. Table 3.5 shows that motorways have the lowest risk of injury accidents of all roads. On average, the rate of injury accidents per million vehicle kilometres of travel on motorways is about 25% of the average for all the public roads. Main roads in rural areas also have a lower accident rate than the average for all public roads.
Part I: 3. Factors Contributing to Road Accidents
61
All roads in urban areas have a higher rate of injury accidents than the average for public roads. The relative accident rate on access roads in urban areas is on average around 7, when the rate on motorways is set equal to 1.00. The variations in accident rate are very consistent across countries. Despite the fact that only seven countries are included in the table, the main tendencies observed are likely to apply to most highly motorised countries. Elements of the design of roads. The design of roads can be described in terms of number of lanes, lane width, horizontal and vertical alignment, design of junctions and numerous other elements. A detailed description of how each element of road design affects the number of accidents is given in Part II, Chapter 1. In this introductory chapter, only a few items will be mentioned. The effect of road width on accident rates depends on whether the road is located in an urban or a rural area. In rural areas, accident rate declines as road width increases, whereas in urban areas, accident rate increases as road width increases. Differences in speed and the mix of traffic using the road may account for this difference in the effect of road width. In rural areas, speed is higher than in urban areas, and a wider road may provide an added margin of safety, which is not as essential in urban areas. In urban areas, more traffic will cross the road than in rural areas. The time it takes to cross a road increases, the wider the road becomes. The number of junctions and access points has a major impact on accident rate. At junctions, the accident rate increases if a junction has more legs and if a higher proportion of traffic enters the junction from the minor road (which by definition would always have less than 50% of the vehicles entering a junction). Both horizontal and vertical alignment affect accident rate. As an example, Figure 3.11 shows the injury accident rate for horizontal curves in Norway depending on the radius of the curve. It is seen that accident rate increases as curves get sharper. In addition to the sharpness of a curve, the number of curves per kilometre of road is related to the accident rate. Surprising curves have higher accident rates than curves that are anticipated by the drivers. The expectations of drivers are affected by the number of curves per kilometre of road. A curve of a given radius has a smaller effect on accident rate on a road with many curves than on a road with few curves. Environmental risk factors. Darkness, precipitation and difficult road surface conditions contribute to increasing the risk of accidents. This has been shown in a number of studies (Hvoslef 1976, Satterthwaite 1976, Sherretz and Farhar 1978, Ivey et al. 1981, Brodsky and Hakkert 1988, Ragnøy 1989, Fridstrøm and Ingebrigtsen 1991, Fridstrøm et al. 1995, Sakshaug and Vaa 1995, Vaa 1995, Johansson 2008,
The Handbook of Road Safety Measures
(injury accidents per mill vehicle km)
Accident rate in curves
62
0.8 0.7 0.6 0.5 0.4 y = 2.3337x-0.4206 R2 = 0.7824
0.3 0.2 0.1 0.0 0
100
200
300
400
500
600
700
800
900
1,000
Radius of curve (metres)
Figure 3.11: Effect of radius of curve on accident rate, based on Norwegian data. Table 3.6: Relative risk of injury accidents in different environmental conditions: estimate for Norway Factor Light conditions
Road surface conditions
Value of factor
Relative accident rate
Confidence interval
Daylight
1.0
Darkness – vehicle accidents
1.0
(0.9; 1.1)
Darkness – pedestrian accidents
2.1
(1.7; 2.5)
Darkness – bicycle accidents
1.6
(1.2; 2.0)
Dry bare road
1.0
Wet bare road
1.3
(1.1–1.8)
Wet snow
1.5
(1.1–2.0)
Snow or ice covered road
2.5
(1.5–4.0)
Wanvik 2009). On the basis of these studies, the relative accident rates in Table 3.6 have been estimated. They are intended to give a best estimate of the relative risk of injury associated with different light conditions and road surface conditions. The risk of accidents increases in the dark, on the wet roads and when the roads are covered with snow or ice. Age and gender of road user. The relationship between the age and gender of road users and their accident rate has been studied extensively. One of the reasons for this is that information about age and gender is usually easily available from accident records, whereas many other human factors are not recorded in official accident records. The
Part I: 3. Factors Contributing to Road Accidents
63
Relative accident rates (safest group = 1)
10 Men Women
9 8 7 6 5 4 3 2 1 0 16-19
20-24
25-34
35-44
45-54
55-64
65-74
75+
Age groups
Figure 3.12: Relative rates of involvement in injury accidents by driver age and gender. Source: see text.
most detailed studies refer to the age and gender of car drivers. Figure 3.12 presents the relationships derived from the following studies: The Netherlands: SWOV (2008) Denmark: Brems and Munch (2008) Norway: Bjørnskau (2008) Sweden: Nilsson (2002) USA: Massie, Green and Campbell (1997) Victoria, Australia: Diamantopoulou, Skalova, Dyte and Cameron (1996) All these studies have investigated the relationship between the age and gender of car drivers and their involvement in injury accidents per miles or kilometres of exposure. In each study, the lowest involvement rate found in any group was set at 1.0. Involvement rates for other groups were then expressed relative to this value. Figure 3.12 presents the average relative accident involvement rate of men and women based on these nine studies. The results are remarkably consistent. Accident involvement rate is a U-shaped function of driver age, both for men and for women. The group with the lowest
64
The Handbook of Road Safety Measures
accident rate is in all studies men at the age of 45–55 or 55–64 years. In young drivers, men tend to have a higher average accident rate than women. From about the age of 30, the mean accident rate is higher for women than for men. The mean accident involvement rate, all ages taken together, is higher for women than for men. This finding will probably strike many readers as surprising. Women are generally considered to be more careful drivers than men and are charged for traffic offences much less often than men. Despite this, there may be a number of reasons why the mean injury accident rate tends to be higher among women than among men. First, women drive less than men. Accident involvement rate per kilometre of driving is not independent of the distance driven, but decreases as driving distances increase. In recent years, an increase in the number of women involved in fatal accidents has been observed. This increase is likely to be due to an increase in exposure, although young women also were found to be increasingly involved in risk-taking behaviour (Romano, Kelley-Baker and Voas 2008). Second, women tend to drive smaller cars than men. Small cars do not give as good protection against injury in an accident as large cars. Third, women tend to drive more in towns and cities, where the risk of accidents is higher than in rural areas. Men, especially young men, were found to be involved in more loss of control accidents and the primary actor in head-on collisions than women (Richardson, Kim, Li and Nitz 1996, Tavris, Kuhn and Layde 2001). These accidents are on average more severe than most other types of accidents. The variation of injury risk by age tends to be U-shaped for pedestrians, cyclists and riders of mopeds and motorcycles as well. As far as bus passengers are concerned, less is known about the variation of injury rate according to passenger age and gender. However, children, teenagers and older women tend to travel by bus more often than other groups in the population, and would therefore be more exposed to risk. Medical condition of road user. Another area where a large amount of research has been reported concerns the effects of different illnesses and health problems on drivers’ risk of accidents. Almost all these studies apply to drivers, and the methodological quality of many studies is rather weak. The results should be interpreted as indications of statistical associations between different health problems and the risk of accidents, not as well-established causal relationships. Table 3.7 is taken from the chapter on health requirements for drivers and shows the association between different illnesses and health problems and the risk of accidents. In the table, the risk faced by healthy drivers is set equal to 1.00, and the risk associated with different illness and health problems is stated relative to this value.
Part I: 3. Factors Contributing to Road Accidents
65
Table 3.7: Association between health problems and drivers’ accident rate: relative accident rates for drivers with certain illnesses (relative accident rate for healthy drivers ¼ 1) Groups of diseases
Best estimate
95% Confidence interval
Renal diseases
0.87
(0.54; 1.34)
Visual impairments
1.09
(1.04; 1.15)
Arthritis
1.17
(1.004; 1.36)
Hearing impairments
1.19
(1.02; 1.40)
Coronary diseases
1.23
(1.09; 1.38)
Diabetes mellitus
1.56
(1.31; 1.86)
Medication/psychoactive substances
1.58
(1.45; 1.73)
Psychiatric diseases
1.72
(1.48; 1.99)
Neurological diseases
1.75
(1.61; 1.89)
Alcoholism
2.00
(1.89; 2.12)
It can be seen from Table 3.7 that a number of illnesses and health problems contribute to increasing the accident rate, but often not by very much. A relative accident rate of less than two indicates a rather weak association, much weaker than the association between driver age and accident involvement rate (Figure 3.12). Drivers probably try their best to compensate for ill health by driving more carefully and perhaps refraining from driving at all in dense traffic at night or when circumstances are otherwise difficult. Impairment through the use of alcohol. Drinking and driving has been recognised as an important road safety problem for a long time. Many studies have reported how alcohol reduces performance. A number of studies, based on roadside surveys, have evaluated the relationship between impairment by alcohol and involvement in road accidents. Results from a sample of these studies are reported in Figure 3.13 based on the following studies: Norway: Glad (1985) Sweden: Nilsson (1986) Norway: Assum and Ingebrigtsen (1990) USA: Zador, Krawchuk and Voas (2000) New Zealand: Keall, Frith and Patterson (2004) Norway: Assum (2005) The Netherlands: Mathijssen (2005)
66
The Handbook of Road Safety Measures
Relative risk (BAC of 0 = 1.0)
1,000
USA (fatalities) USA (all) Norway (fatalities) Norway (injuries) Netherlands (injuries) New Zealand (fatalities) Sweden (injuries)
100
10
1 0.0
0.5
1.0
1.5
2.0
2.5
3.0
BAC-level (mg/ml)
Figure 3.13: Association between blood alcohol level and relative accident involvement rate. Source: see text. Although there is considerable variation in the risk estimates, especially at high BAC levels, it is beyond dispute that accident rate increases dramatically as blood alcohol level increases. Note the use of a logarithmic scale for relative accident involvement rate in Figure 3.13. There are few, if any, other risk factors that have been found to increase accident rate by a factor of more than 100. The increase of accident rate has been found to be steeper for injury accidents and fatal accidents than for propertydamage-only accidents. Speed of travel. Speed is an important risk factor. A large number of studies have evaluated the effects of changes in traffic speed on the number and severity of accidents. Considerably, fewer studies have evaluated how the choice of speed made by each driver affects the accident rate of that driver. Studies that are often quoted include those of Solomon (1964), Munden (1967), Cirillo (1968), West and Dunn (1971), Harkey, Robertson and Davis (1990) and Kloeden, Ponte and McLean (1997, 2001). With the exception of the two studies by Kloeden et al., all these studies suggested that drivers who drive more slowly than the mean speed of traffic and drivers who driver faster than the mean speed of traffic have a higher rate of accident involvement than drivers whose speed is close to the mean speed of traffic. It has been shown (White and Nelson 1970) that errors in speed measurements can generate an artificial U-shaped curve for the relationship between deviation from mean
Part I: 3. Factors Contributing to Road Accidents
67
speed and relative rate of accident involvement. Another potential source of error is the precise definition of the accident involvement variable. If this variable is defined in terms of the number of cars involved in accidents, all accidents that involve two cars will be counted twice. Finally, if estimates of the pre-crash speeds of vehicles involved in accidents are less precise than estimates of the speeds of vehicles not involved in accidents, this could generate an artificial U-shaped relationship between speed and accident involvement. On the whole, there is therefore a significant chance that the findings of the studies indicating a U-shaped relationship between speed and accident involvement are to a large extent attributable to methodological weaknesses of the studies.
3.8 A
SURVEY OF RISK FACTORS FOR INJURY SEVERITY
Type of motor vehicle – vehicle mass. The significance of the mass of the vehicle and whether one is protected by the body of the vehicle or not are described in Section 4.19 of Part II. It is well known that the greater the mass, the more protection people have against being injured in accidents. According to official Norwegian accidents statistics, the importance of mass and a surrounding car body can be shown by studying how the probability of being injured, given that a driver, pedestrian or cyclist is involved in an injury accident reported to the police, varies between different groups of road users and types of vehicle. This is possible because uninjured drivers who are involved in injury accidents (including uninjured pedestrians and cyclists) are recorded in official accident statistics. Figure 3.14 shows how the probability of being injured as a driver when one is involved in an injury accident reported to the police varies between road user groups and types of vehicle. The figure is based on official Norwegian accident statistics. Among pedestrians and cyclists, more than 95% of those who are involved in injury accidents reported to the police are injured. Among people riding mopeds and motorcycles, the proportion injured is around 90%. About 45% of car drivers involved in injury accidents reported to police are injured. Among drivers of trucks or buses, the proportion injured is between 10% and 20%. These differences would be even more distinct if property damage only accidents were included. The statistics that are available for property damage accidents, however, do not allow such a detailed description of vehicle types as the injury accident statistics. The differences in the mass of passenger cars do not matter very much for the overall number of injured car occupants. Occupants are better protected in large cars than in small cars. However, the protection a large car offers is at the expense of those who use
68
The Handbook of Road Safety Measures
Relationship between typical mass of vehicle and probability of driver (pedestrian, cyclist) injury in injury accidents Pedestrian
100,0
Bicycle Large motorbike Moped Small motorbike
Probability of getting injured
90,0 80,0 70,0 60,0 50,0
Passenger car
40,0
Van Station wagon
30,0
Truck
Taxi
20,0
Bus
10,0 0,0 0
2000
4000
6000
8000
10000 12000 14000 16000 18000 20000
Typical mass (kilograms)
Figure 3.14: Relationship between mass of vehicle (or road user) and probability of getting injured when involved in an injury accident. Based on Norwegian accident statistics.
smaller cars. The risk of a driver being injured is reduced by almost 50% if the mass of the car increases from less than 850 kg to more than 1,500 kg. At the same time, a car of more than 1,500 kg poses a 75% higher risk of injuring other cars than a car of less than 850 kg. Impact speed. Figure 3.15 shows how the probability of serious injury to drivers of private cars in a traffic accident depends on impact speed (DV or the change in speed that occurs on impact). The figure is taken from Evans (1996) and shows how the probability of being seriously injured varies by changes in speed for drivers with and without seat belts. Figure 3.15 shows that up to an impact speed of around 70 km/h, it is more likely that a serious injury will be avoided than not. When impact speed is above 100 km/h, it is impossible to avoid serious injury, whether or not seat belts are worn. Serious injuries are roughly defined here as injuries that require hospitalisation. The relationship between impact speed and the probability of being injured has the same form for pedestrians and cyclists as for car drivers, but shifts towards a lower speed. For pedestrians, there is a considerable increase in the chances of being killed when impact speed is more than 30 km/h (Pasanen 1996).
Part I: 3. Factors Contributing to Road Accidents
69
Probability of serious driver injury
Probability of serious driver injury depending on impact speed 1.0 0.9 0.8 0.7 0.6
No seat belt Seat belt worn
0.5 0.4 0.3 0.2 0.1 0.0 0
20
40 60 80 Impact speed (km/h)
100
120
Figure 3.15: Relationship between impact speed and probability of serious driver injury (Evans 1996). Wearing personal protective equipment. Use of personal safety equipment is of considerable importance for the probability of being injured in a traffic accident, especially for otherwise unprotected road users, that is, pedestrians, cyclists or riders of mopeds or motorcycles. A moped or motorcycle rider reduces the probability of being injured by around 25% by wearing a helmet. If protective leather clothing is also worn, the probability of being injured is reduced by a further 30%. Together, such protective equipment gives reduction in the probability of being injured of approximately 50% [1–(0.75 0.70)]. A pedestrian who uses a reflector reduces the probability of being hit in the dark by 70–90%. A car occupant wearing a seat belt has a 20–30% lower probability of being injured than a car occupant not wearing a seat belt, and a 40–50% lower probability of being killed.
3.9 ASSESSING
THE RELATIVE IMPORTANCE OF RISK FACTORS
Describing factors contributing to road accidents and injuries by listing factors that have been found to be statistically associated with the frequency of accident occurrence and the severity of injuries sustained in accidents represents only the rudiments of a scientific explanation. Can the importance of factors contributing to road accidents be
70
The Handbook of Road Safety Measures
assessed more formally? Is it possible to give a more coherent scientific account of various aspects of road safety problems? It has sometimes been claimed that there is no rational way of defining road safety problems and assessing how easily these problems can be solved. Thus, for example, Pedersen, Elvik and Berard-Andersen (1982, page 29) state: ‘‘What do we mean by a road safety problem? There are many answers to this question that all make sense. The risk that children run on their trip to school is a road safety problem. Drinking and driving is a road safety problem, driving in the dark is a road safety problem, and young driver risk is a road safety problem. By making such lists of problems, it is possible to cover all areas of road safety. The snag is that the various problems on such a list tend to overlap. Children are at risk when travelling to school partly because young drivers have a high risk of accident involvement, partly because of drinking and driving, and partly because driving in the dark increases the risk of accident. Drinking and driving is a major problem partly because it takes place in the dark and on roads where there are pedestrians and cyclists. These examples show how difficult it is to define road safety problems in an orderly and logical way. This difficulty is particularly relevant when we want to give an exhaustive definition of road safety problems.’’ It is obviously correct that the risk factors that contribute to accidents and injuries interact in complex ways that are not fully known. It is equally true that there does not exist any scientifically ‘correct’ way of defining road safety problems, at least not in a strict sense of the term. But it is wrong to conclude that any list of factors contributing to road accidents is completely arbitrary and therefore of no use either as a scientific explanation or for the purpose of developing an effective road safety programme. A rational approach to assessing the importance of risk factors in contributing to accidents and injuries can be developed by relying on concepts taken from epidemiology. Describing the importance of risk factors in terms of attributable risks. One of the basic notions of epidemiology is that of attributable risk, also known as etiologic fraction (Kleinbaum, Kupper and Morgenstern 1982). Attributable risk is the fraction of accidents or injuries that is attributable to a certain risk factor, that is, the size of the reduction in the number of accidents or injuries that could be achieved by removing the risk factor. Attributable risk is generally expressed as a fraction and assumes values in the range from 0 to 1. To illustrate the concept, consider the case of unprotected road users. Unprotected road users are all road users who are not enclosed by a deformable
Part I: 3. Factors Contributing to Road Accidents
71
Table 3.8: Number of fatalities, police reported injuries, amount of travel and relative risks of fatality and injury for unprotected and protected road users in Sweden (Thulin and Nilsson, 1994) Injuries, travel and risk Killed road users
Unprotected road users
Protected road users
All road users
259
466
725
Injured road users
6,454
14,633
21,087
Mill person km of travel
6,661
114,861
121,522
Relative fatality rate
9.58
1.00
1.47
Relative injury rate
7.61
1.00
1.36
Attributable fatality risk
0.896
Reference
0.320
Attributable injury risk
0.869
Reference
0.266
structure absorbing energy in case of an accident. They include pedestrians, cyclists and riders of mopeds and motorcycles. Protected road users are drivers and passengers using cars and buses. Table 3.8 shows the number of fatalities, the number of injuries recorded by the police, the annual amount of travel and the relative fatality and injury rate of unprotected and protected road users in Sweden according to the 1992 National Household Travel Survey (Thulin and Nilsson 1994). This table can be used to illustrate the meaning of the concept of attributable risk. It is seen that unprotected road users run a risk of being killed that is nearly 10 times higher than the risk run by protected road users. To bring down the fatality risk to unprotected road users to the same level as that of protected road users, a reduction of nearly 90% would be needed. This is the attributable risk of a fatal injury for unprotected road users as shown in the table (0.896), which is estimated simply as the ratio (9.58–1)/9.58. This measure of attributable risk will be referred to as the target attributable risk, that is, the reduction in risk that must be achieved within the target group (in this case unprotected road users) in order get the same risk level as the reference group (in this case protected road users). The overall or population attributable risk (PAR) for unprotected road users is the contribution that their enhanced risk level makes to the total number of people killed or injured. The overall attributable risk has been estimated at 0.320 for fatality risk and 0.266 for injury risk, as shown in Table 3.8. To explain how PAR is estimated, denote the proportion of exposure to the risk factor of interest by PE. In Table 3.8, this proportion is 6,661/121,522 ¼ 0.055, that is the proportion of all travel done by unprotected road users. Denote by RR the relative risk
72
The Handbook of Road Safety Measures
run when exposed to the risk factor of interest. In Table 3.6, this is 9.58 for fatality risk. In computing relative risk, the safest category of any risk factor is always used as reference. PAR is then defined as: PAR ¼
PEðRR 1Þ ½PEðRR 1Þ þ 1
For fatality risk in Table 3.8, the numerator comes to 0.055 8.58 ¼ 0.47. The denominator is 0.47þ1 ¼ 1.47. Attributable risk is 0.47/1.47 ¼ 0.32. By estimating the risks attributable to various risk factors, it is possible to obtain a quantified notion of their importance in contributing to accidents and injuries. This makes it possible, in principle, to rank various risk factors in order of their importance as contributing factors to accidents and injuries. In a report written for the Swedish National Roads Administration (Elvik and Amundsen 2000), an attempt was made to quantify the importance of various road safety problems in Sweden by estimating the risks attributable to them. Several notes of caution with respect to the use of attributable risks as a measure of the importance of various road safety problems are pertinent:
There are many important risk factors for which no meaningful estimate of attributable risk is possible. Inattention on the part of road users is a case in point. There is little doubt that inattention causes many accidents. However, trying to quantify the contribution of this risk factor to accidents is very difficult because exposure to it is virtually impossible to measure. (What is the proportion of kilometres driven by inattentive drivers?) Risk factors tend to be correlated, but these correlations are not very well known. In most cases, it is probably not correct to add the risks attributable to two risk factors to find their joint contributions to accidents or injuries. Some road safety problems are not adequately described in terms of enhanced risk. Children, for example, do not have an excessive risk of injury in traffic compared with adults. However, there is a strong desire to provide a higher level of safety for children than for other groups of road users. As long as it remains possible to reduce the risk of injury to children, accidents will be regarded as a problem, despite the fact that estimates of attributable risk will not identify children as a group exposed to a particularly high risk. Accidents and injuries are not fully reported in official accident statistics. If the level of reporting is associated with a risk factor, an estimate of the risk attributable to that factor will be biased. This may apply to the risk attributable to being an unprotected road user, at least as far as injuries is concerned. Injuries to
Part I: 3. Factors Contributing to Road Accidents
73
unprotected road users, especially cyclists, are known to be less fully reported in official statistics than injuries to car occupants. An estimate of attributable risk based on official accident statistics will then be an underestimate of the true risk attributable to being an unprotected road user. Despite these limitations, the concept of attributable risk is fruitful when trying to assess the importance of various road safety problems. The contribution of a number of risk factors to the current number of road accident fatalities and injuries in Sweden was therefore estimated by putting together information from a large number of sources. Correlations among risk factors. When the contributions of various risk factors to current road safety problems are to be assessed, it is important to try to account for the correlations between risk factors. Otherwise, the contributions of a set of risk factors to injuries may be counted twice, and thereby the potential for injury reduction by removing or controlling the risk factors may be overestimated. This can be illustrated by means of a numerical example. Consider the data given in Table 3.9. These data are fictitious and are used only to illustrate how the existence of correlations between risk factors may bias estimates of the risks attributable to them. In this table, the risks attributable to two risk factors are considered. It is assumed that each risk factor increases risk by 50%, which gives a relative risk of RR ¼ 1.50 for those who are exposed to the risk factor. Relative risk when exposed to both risk factors is 1.5 1.5 ¼ 2.25. Moreover, it is assumed that 20% of all road users are exposed to each factor. If exposure to one of the risk factors is independent of exposure to the other, it can easily be calculated that 64% of all road users will not be exposed to any of the two factors (0.8 0.8 ¼ 0.64). Thirty-two per cent will be exposed to one factor only [2 (0.8 0.2 ¼ 0.16)], and 4% will be exposed to both factors (0.2 0.2 ¼ 0.04). This distribution of exposure is shown in the first row of Table 3.9, for the case, in which there is no correlation in exposure to the two risk factors. In this case the risk, attributable to both risk factors is 0.174. The crude estimate of the risk attributable to each risk factor is 0.115. The sum of the crude attributable risks is 0.230, which is more than the total risk attributable to both risk factors. The source of the problem is the fact that crude estimates counts the cell in which both risk factors are present twice. This double counting gets worse as the correlation between the risk factors gets stronger. Attributable risks are shown in bold italics in Table 3.9. As shown in Table 3.9, estimates of the risk attributable to each risk factor grow to 0.138 for a moderate correlation in exposure to the two risk factors, to 0.160 when
74
The Handbook of Road Safety Measures
Table 3.9: Illustration of how the existence of correlations between risk factors may bias estimates of risks attributable to them (fictitious data) Risk factors present in exposure and injuries Correlation in exposure None (0.00–0.15)
Exposure and risk
None
Factor 1
Factor 2
Both
Total
Exposure
640
160
160
40
1000
Injuries
640
240
240
90
1210
True relative risk
1.00
1.50
1.50
2.25
Crude risk attributed to factor 1
1.00
1.65
Crude risk attributed to factor 2
1.00
0.115 1.65
Total attributable risk Moderate (0.15–0.50)
Strong (0.50–0.85)
Perfect (0.85–1.00)
0.115 1.58
0.174
Exposure
680
120
120
80
1000
Injuries
680
180
180
180
1220
True relative risk
1.00
1.50
1.50
2.25
Crude risk attributed to factor 1
1.00
1.80
Crude risk attributed to factor 2
1.00
Total attributable risk
1.00
Exposure
720
80
Injuries
720
120
True relative risk
1.00
1.50
1.50
2.25
1.95
Crude risk attributed to factor 1
1.00
Crude risk attributed to factor 2
1.00
Total attributable risk
1.00
Exposure
760
40
Injuries
760
60
True relative risk
1.00
1.50 2.00
Crude risk attributed to factor 1
1.00
Crude risk attributed to factor 2
1.00
Total attributable risk
1.00
0.138 1.80
0.138 1.69
0.180
80
120
1000
120
270
1230 0.160
1.95
0.160 1.82
0.187
40
160
1000
60
360
1240
1.50
2.25 0.167
2.00
0.167 2.00
0.194
exposure is strongly correlated and to 0.167 when exposure is very strongly correlated. Thus, crude estimates of both relative risk and attributable risk become more and more biased when the correlation between risk factors is stronger. One would accordingly expect that estimates of risk that are adjusted for correlations with other risk factors are lower than crude estimates. Ideally speaking, estimates of the risks attributable to specific risk factors ought to be derived from multivariate analyses, in which the partial effects of each risk factor have been estimated while controlling for as many other risk factors as possible. Very few
Part I: 3. Factors Contributing to Road Accidents
75
multivariate analyses of this nature, based on Swedish data, are available (Fridstrøm et al. 1993, Tegne´r and Loncar-Lucassi 1996). Besides, the results of those few studies have not been presented in a format that easily allows attributable risks to be estimated. It must be therefore concluded that studies allowing well-controlled estimates of the contributions of various risk factors to injuries in road accidents do not exist. Any estimate based on available data is likely to be incomplete and possibly misleading. Nevertheless, it has been concluded that it is better to try to make the best use of current data, rather than not attempting to quantify the contributions of various risk factors to current road problems. Quantification of the importance of risk factors – a case illustration. The contribution of various factors to current road safety problems in Sweden was estimated in stages. Details of the estimation are given in the full report in Elvik and Amundsen (2000). The following points are important to note with respect to the interpretation of the estimates that are presented:
The attributable risks refer to the risks of sustaining a fatal injury or an injury reported to the police. No account has been taken of incomplete accident reporting. The assessment is limited to a total of 20 risk factors for which data of acceptable quality could be found. The estimates of attributable risk represent the contributions of the various factors as of the early or mid nineteen nineties. The contribution of a specific risk factor to fatalities and injuries may change over time.
Simple first-order attributable risks. The first stage of analysis was to estimate the firstorder attributable risks of the risk factors included in the study. These estimates did not account for overlaps between types of accidents affected by various risk factors or correlations in exposure to the various risk factors. The results of this first stage of estimation are given in Figure 3.16. To keep the figure simple, confidence intervals are not shown. Risk factors have been assigned to five main groups:
Bad system design, which includes risk factors related to the design of roads and the traffic environment Environmental risks, which includes the effects on accidents of daylight and weather Vulnerability of road users, which includes the enhanced risk run by pedestrians, cyclists and inexperienced drivers Unsafe road user behaviour, which comprises violations of road traffic legislation Provision of medical services, which refers to the limited availability of rapid rescue services in remote and sparsely populated areas of Sweden.
76
The Handbook of Road Safety Measures
First order attributable risks
2.5
2.36
Fatalities Injuries
2.0 1.56 1.5
1.0 0.56 0.5
0.37
0.38 0.29
0.63 0.46
0.63 0.37 0.17 0.07
0.0 Bad system design
Environmental risks
Vulnerability Unsafe of road road user users behaviour
Insufficient rescue services
All areas
Contributions of various categories of road safety problems
Figure 3.16: First-order attributable risks of major road safety problems in Sweden. Bad system design, vulnerability of road users and road user behaviour are the three most important main categories of roads safety problems as evidenced in the first-order risks attributable to these problems. Each of these problems accounts for about 60% of fatalities and about 40% of all injuries. If violations of road traffic law did not occur, the number of fatalities could be reduced by 63% and the total number of injuries could be reduced by 37% (Figure 3.16). The sum of all first-order attributable risks is 2.36 for fatalities and 1.56 for injuries. This simply demonstrates the widely known fact that more than one risk factor may contribute to each accident. This fact is known from in-depth studies of accidents, which usually list several factors that may have contributed to each accident. Hence, if the contributions of a set of risk factors to accidents are simply added, the answer will nearly always come to more than 100%. In fact, if more factors had been included in the analysis, the sum of the first-order contributions would have been greater than the numbers given in Figure 3.16. The single most important contributing factor out of the 20 factors that were included in the analysis is violation of speed limits. Speeding represents an attributable risk of 0.38 for fatalities and 0.21 for injuries.
Part I: 3. Factors Contributing to Road Accidents
77
Adjusting for overlapping problem definitions and correlations among risk factors. The second stage of the analysis was to adjust the simple first-order attributable risks for overlapping types of accidents affected and the presence of correlations in exposure to the various risk factors. The following categories of road safety problems partly overlap:
High risk in urban areas and high risk for unprotected road users: Since most unprotected road users are injured in urban areas, the higher risk in urban areas compared with the countryside is partly attributable to the presence of more unprotected road users in urban areas. The overlap between these problems was removed by subtracting from the risk attributed to urban areas the proportion due to accidents involving unprotected road users. High risk in urban areas and high risk in some junctions: Most accidents in junctions occur in urban areas and thereby contribute to the high risk in those areas. The overlap was removed by subtracting from the risk attributed to junctions the proportion of junction accidents that occur in urban areas. Roads standards are low and some roads are poor with respect to crashworthiness: These problems partly overlap. The overlap was removed by subtracting from the risk attributed to roadside objects the part that refers to narrow roads with poor alignment. High risk for unprotected road users and safety problems of children: These problems overlap to the extent that children are injured as unprotected road users. Risks attributable to children as unprotected road users were subtracted from the overall risk attributed to children to remove the overlap between the problems. High risk for unprotected road users and for older road users: These problems overlap to the extent that older road users are injured as pedestrians and cyclists. Removing the overlap involved subtracting from the overall risk attributed to being an older road user the part that was due to being unprotected as well.
It was assumed that exposure to darkness and to winter road conditions are correlated. This assumption is reasonable because there is less daylight in winter than in summer. A correlation of 0.5 was assumed, and the first-order attributable risks from darkness and winter conditions were, somewhat conservatively, reduced by 30% each. Violations of road traffic law, except for speeding, were assumed to be correlated. A three-way (multiple) correlation of 0.3 between drinking and driving, not wearing seat belts and other violations was assumed. The risk attributable to each of these violations was reduced by 10%.
78
The Handbook of Road Safety Measures
Attributable risks
2.5
2.36 Adjustment for overlapping problem categories and correlations between risk factors 1.95
2.0 1.5
1.56
Adjustment for overlapping residuals 1.23
1.0
0.89
0.73
0.5
Fatalities Injuries
0.0 Simple first order effects
Adjusted first order effects
Adjusted marginal effects
Stages in estimating attributable risks
Figure 3.17: Adjustments in first-order attributable risks. Once these adjustments had been made, the marginal effect of each risk factor was estimated by applying the method of joint residuals. Figure 3.17 shows the results of the estimation. Adjusting for overlaps and correlations reduces the sum of the attributable risks from 2.36 to 1.95 for fatalities and from 1.56 to 1.23 for injuries. The sum of the marginal effects of the risk factors is 0.89 for fatalities and 0.73 for injuries. By definition, the sum of marginal effects cannot exceed 1, since it is logically impossible to reduce the number of injuries by more than 100% (which equals a proportion of 1). Figure 3.17 shows that by removing all risk factors included in this analysis, it is theoretically possible to reduce the number of fatalities by 89% and the total number of injuries by 73%. In practice, of course, the potentials for reduction are smaller. It is almost never possible to entirely remove a risk factor. Marginal contributions of various risk factors. Table 3.10 presents estimates of the marginal contributions of various risk factors to current road safety problems in Sweden. The marginal contribution of a risk factor to fatalities and injuries denotes the partial contribution it represents in a set of risk factors whose combined effects have been estimated according to the method of joint residuals. If the problems are ranked according to the size of their contributions to fatalities and injuries, the following five problems are at the top of the list:
Speed limit violations (0.172 for fatalities; 0.125 for injuries) Poor vehicle crashworthiness (0.156 for fatalities; 0.039 for injuries)
Part I: 3. Factors Contributing to Road Accidents
79
Table 3.10: Adjusted marginal attributable risks for various risk factors in Sweden (Elvik and Amundsen 2000) Marginal attributable risk Exposed group
Comparison group
Fatalities
Injuries
1. Roads in urban areas
All roads in rural areas
0.060
0.127
2. Poor road standard
Motorway road standard
0.016
0.006
3. Roadside obstacles
Clear side zones
0.070
0.021
4. Poor vehicle crashworthiness
Best performing cars
0.156
0.039
5. Erroneous highway signs
Correct highway signs
0.005
0.009
6. Heavy vehicles
Light vehicles
0.049
0.003
7. High risk junctions
Low risk junctions
0.010
0.004
A. Bad system design (total)
Good system design
0.245
0.210
8. Risk at night
Risk during daytime
0.053
0.045
9. Risk in winter
Risk in summer
0.063
0.061
10. Risk of animal crashes
Zero risk of animal crashes
0.007
0.023
B. Environmental risks (total)
Less hazardous environment
0.123
0.128
11. Children’s traffic risks
Safest age group (any mode)
0.005
0.005
12. Unprotected road users
Protected road users
0.081
0.052
13. Young drivers’ traffic risks
Safest age group of drivers
0.039
0.060
14. Older road users’ traffic risks
Safest age group (any mode)
0.044
0.025
C. Vulnerable road users (total)
Safest groups of road users
0.169
0.142
15. Speed limit violations
Legal speed
0.172
0.125
16. Drinking and driving
Sober driving
0.030
0.026
17. Not wearing seat belts
Wearing seat belts
0.035
0.017
18. Other violations of traffic law
Behaviour complying with the law
0.038
0.033
19. Excessive driving in towns
Removal of 3% of urban driving
0.002
0.009 0.209
D. Unsafe behaviour (total)
Safe road user behaviour
0.277
20. Standard of medical services
Ambulance helicopters
0.076
0.042
E. Current rescue services (total)
Best available service level
0.076
0.042
All problem areas
Best currently available safety
0.890
0.730
High risks of unprotected road users (0.081 for fatalities; 0.052 for injuries) Insufficient medical and rescue services for accident victims (0.076 for fatalities; 0.042 for injuries) Roadside obstacles (0.070 for fatalities; 0.021 for injuries).
The problems have been ranked according to their contributions to fatal injuries. High risk in urban areas makes a major contribution to injuries in general, but is actually a
80
The Handbook of Road Safety Measures
safety factor for fatalities. This means that the risk of injury is higher in urban areas than outside, but that the risk of fatal injury is lower in urban areas than outside. This example indicates that, despite the great limitations in available data, it is possible to gain an impression of the relative importance of various risk factors in contributing to road accident fatalities and injuries. To make use of available data, several adjustments had to be made, but these were to some extent based on official accident data. The next step of an analysis in which the importance of risk factors has been assessed would be to try to sort risk factors into categories with respect to how easily the factors can be modified or influenced by means of road safety measures.
4.
B ASIC C ONCEPTS R ESEARCH 4.1 RANDOM
OF
R OAD S AFETY
AND SYSTEMATIC VARIATION IN ACCIDENT COUNTS
Road safety is usually defined and evaluated in terms of the recorded number of accidents or the number of killed or injured road users. The number of accidents or injured road users recorded during a certain period is the result of a complex process. There are two problems associated with the use of the recorded number of accidents to estimate safety: under-reporting of accidents (see Chapter 3) and random variation in the recorded accident numbers. When looking for explanations of accidents and for ways of preventing them, it is important not to mix up random and systematic variation in the number of accidents. Systematic variation is the ‘true’ variation in the accident counts, i.e. variation of the expected number of accidents. Random variation is variation of the observed accident counts around the expected number of accidents. These concepts are described in more detail below. When evaluating safety measures, it is often better to use estimates of the expected, rather than the recorded, number of accidents by using the Empirical Bayes (EB) method, which also is described below. Expected number of accidents. The expected number of accidents is the number of accidents (e.g. on a specific road or in a specific junction) that one can expect per time unit, based on known properties of the road or junction. It is the average number of accidents that will occur per unit of time in the long run, given that exposure and all risk factors remain constant.
The Handbook of Road Safety Measures Copyright r 2009 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISBN: 978-1-84855-250-0
82
The Handbook of Road Safety Measures
Recorded number of accidents during eight years at a junction and mean of the annual numbers
Number of accidents
4
3 Recorded number Annual mean
2
1
0 0
1
2
3
4
5
6
7
8
9
Year
Figure 4.1: Illustration of the concept of the expected number of accidents. The meaning of the expected number of accidents can be clarified by means of an example. Figure 4.1 shows hypothetical numbers of accidents recorded in a junction during a period of eight years. The black dots represent the recorded number of accidents per year and the white dots show the moving average of the annual counts of accidents. In the first year, this is the same as the count of accidents for that year. In the second year, it is the average of the two first years; in the third year, it is the average of first three years, etc. It can be seen that the recorded number of accidents in a given year is not necessarily representative of the mean annual number of accidents at the junction in the period we are studying. We also see that as accident counts are accumulated for more years, the annual average number becomes more stable and less affected by the recorded number in a single year. If one were to collect accident data for the same junction over a very long period, e.g. 50 or 100 years, the annual average number of accidents for this period would eventually hardly be affected at all by the recorded number of accidents for a given year. In the limit, the annual average would be insensitive to the recorded number of accidents in a specific year. It would then be an estimate of the long-term expected number of accidents. This is the average number of accidents per unit time, which would be expected to occur in the long run at a constant exposure (amount of traffic) and at a constant accident rate per unit of exposure. However, during such a long period, it cannot be assumed that the junction has an unchanged amount of traffic or otherwise remains unchanged. Thus the expected
Part I: 4. Basic Concepts of Road Safety Research
83
number of accidents would not remain constant during such a long period. In practice, the expected number of accidents is seldom estimated by observing the accident history for a single junction, a single road section, or a single driver for a period of 50 or 100 years. The true value of the expected number of accidents for a given unit of observation, such as a junction or a driver, cannot be observed directly but has to be estimated. The most common method of estimating the expected number of accidents is to study a large number of units (junctions, road sections, drivers, vehicles, etc.), which vary with respect to characteristics that are believed to influence the expected number of accidents. By means of statistical analysis, we then try to determine the amount of systematic variation in accident counts and identify factors that produce it. Random and systematic variation in accident counts. There is systematic variation in the number of accidents when some units (junctions, drivers, vehicles, roads) have a higher or lower long-term expected number of accidents than other units of the same type. Random variation in the number of accidents is variation in the recorded number of accidents around a given expected number of accidents. Two sets of factors generate systematic variation in the number of accidents:
The amount of traffic (exposure) Risk factors (factors that affect the probability of accidents at a given exposure).
On top of these come vehicle occupancy and other factors that influence the number of injury victims per accident. The fact that road accidents are subject to random variation means that not all changes in the recorded number of accidents imply changes in the expected number of accidents. For example, a decrease from 280 fatalities per year to 250 is not more than random variation. A decrease from 10,000 injured to 9,500 people injured is large enough for it not to be exclusively attributable to chance. The problem of not mixing up random fluctuations of the number of accidents with changes in the long-term expected number of accidents is most severe when the mean expected number of accidents per study unit is small. To illustrate the problem, consider the hypothetical case of 100 junctions that have a mean expected number of accidents of 1.5 per junction per year. Assume further that the recorded number of accidents in any junction is the result of pure random variation, i.e. all junctions have the same expected number of accidents. Suppose a road safety measure reduces the mean expected number of accidents per junction per year to 1.0, still assume a random distribution of accidents in the set of junctions.
84
The Handbook of Road Safety Measures
A simple before-and-after study will then most likely observe a reduction of the recorded number of accidents in 50 junctions, an increase in 24 junctions, and an unchanged number of accidents in 26 junctions. Apparently, the safety measure will be most effective in those junctions that had the highest recorded number of accidents in the year before. In the 20 junctions that had three or more accidents in the before-period, and a total of 69 accidents, one would find a reduction to 17 accidents (a 75% reduction). This appears to be a far greater reduction than the mean reduction of 33% (from a total of 150 to 100 accidents). Such an impression is, however, misleading. Under the assumptions made in this example, the true effect is identical in all junctions – any observed variation in effect is random only. Identifying junctions where the safety measure was particularly effective, based on the recorded number of accidents in the before-period, would be capitalising on chance. Statistical modelling of systematic and random variation in accident numbers. Pure random variation in accidents is usually modelled by the Poisson probability law. According to the Poisson probability law, the variance of the count of accidents equals the mean. The smaller the size of the standard deviation, calculated as a percentage of the number of accidents, the greater the number is. For example, the standard deviation in 10 accidents is equal to about three accidents, i.e. 30%. The standard deviation in 100 accidents is equal to 10 accidents, i.e. to say 10%. A 95% confidence interval for random variation in the number of accidents can be obtained by multiplying the square root of the number of accidents by 1.96. For example, the 95% confidence interval for an expected number of accidents of 10 is 10 1:96
p
10 ¼ 10 1:96 3:16 ¼ 10 6:2
The lower limit of the confidence interval is 3.8 and the upper limit is 16.2. Multivariate statistical models, often Poisson regression models or negative binomial regression models, are increasingly used to analyse factors that explain systematic variation of the number of accidents. The most common specification of these models is Expected number of accidents ¼ aQb exp
P
kx
where Q measures exposure, i.e. some variable describing traffic volume. Exp is the exponential function, i.e. the base of natural logarithms (e ¼ 2.71828) raised to the sum of parameter estimates multiplied by the relevant values of the explanatory variables, representing risk factors (Skx). For an in-depth presentation of multivariate accident modelling, the reader is referred to Gaudry and Lassarre (2000).
Part I: 4. Basic Concepts of Road Safety Research
85
Modelling the expected number of accidents in before-and-after studies with the Empirical Bayes method. Results from before-and-after studies may be misleading when evaluation studies are based on the recorded number of accidents, especially when the recorded number is small, and when the study units selected had higher than normal recorded numbers of accidents in the before period. When a measure is implemented only for units with high numbers of accidents in the before-period, the number of accidents will most likely be smaller in the after period, even if the measure has no effect at all. This is referred to as the regression to the mean effect. Regression to the mean may be controlled by using the expected, instead of the recorded, number of accidents in the before-period. Since the expected number is never known exactly, it has to be estimated. By means of the EB method, the expected number of accidents (e.g. on a road section or at a junction) can be estimated as follows:
It is estimated how many accidents would normally be expected in a unit with comparable properties (risk factors and exposure), based on a multivariate model of accident occurrence in a (preferably large) number of the same type of units, with varying properties. In addition to the normal expected number of accidents, the uncertainty of this estimate is calculated. It is estimated how many accidents would be expected for the actual unit, by combining the normal expected number of accidents (step 1) and the recorded number of accidents. The observed number of accidents is included in order to take into account specific unobserved risk factors (that are not included in the accident model in step 1). The expected number of accidents is assigned a statistical weight that corresponds to the uncertainty of this estimate and that can assume values between 0 and 1. The expected number of accidents for the specific unit is calculated as follows:
Expected number of accidents for the specific unit ¼ Expected number of accidents Statistical weight þ Observed number of accidents ð1 Statistical weightÞ
The observed number of accidents in the after period is compared to the expected number of accidents that has been estimated for the specific unit in the before period.
A more detailed description of the EB method, the statistical background and applications are given in Hauer (1997).
86
The Handbook of Road Safety Measures
4.2 THE
USE OF ACCIDENT RATES TO MEASURE SAFETY
It has traditionally been assumed that the effects of traffic volume on the number of accidents can be removed – controlled for – by estimating an accident rate: Accident rate ¼
Number of accidents Traffic volume
This assumption is not correct (Hauer 1995). Most accident rates, which are defined per vehicle kilometre or per person kilometre, have a significant non-linearity, i.e. the assumption that the number of accidents is independent of the distance driven or the amount of travel does not hold. Figure 4.2 shows a very striking example of this, taken from a British study (Forsyth, Maycock and Sexton 1995). Accident rate declines sharply as annual driving distance increases. The mean accident rate for men is 0.345 and for women is 0.389. Women have a higher mean accident rate than men, despite the fact that their accident rate for any given annual mileage is lower than the accident rate for men. If this fact were not known, one might erroneously conclude that women are poorer drivers than men.
Accident rate (accidents per million miles)
The finding presented in Figure 4.2 is a case of Simpson’s paradox, which may occur when data exhibiting strong non-linearity, or a strong interaction between two or more Relationship between annual driving distance and accident rate (Forsyth, Maycock and Sexton, 1995) 250 200 150
Men Women Mean for women: 38.9 at 4,766 miles per year
100
Mean for men: 34.5 at 8,350 miles per year 50
0 0
5,000
10,000
15,000
20,000
25,000
Annual driving distance (miles)
Figure 4.2: Relationship between annual driving distance and accident rate (Forsyth Maycock and Sexton 1995).
Part I: 4. Basic Concepts of Road Safety Research
87
factors, are aggregated across categories of the non-linear function or the variables that interact. This can result in a fallacy of aggregation: in this case to an erroneous conclusion that the accident rate for women is higher than it is for men. Non-linear relationships between traffic volume and accidents have also been found in most studies that have developed accident models for roads or junctions. In most studies, the percentage increase of the number of accidents is smaller than would be expected if there were a linear relationship, i.e. the number of accidents increases at a lower rate than traffic volume (see also Section 3.4). Consequently, the effects of exposure on accidents are not adequately controlled by estimating accident rates and accident rates may have limited value as a measure of road safety. Road safety evaluation studies that use accident rate ratios as the dependent variable are of dubious validity unless the accident rate ratio applies to study units that have an identical amount of exposure and are otherwise identical with respect to at least major risk factors affecting the number of accidents.
4.3 EXPLAINING
ROAD ACCIDENTS
–
THE CONCEPT OF CAUSE
Do accidents have causes? If they do, how can we make sense of the term ‘cause of accident’? Until around 1960, it was widely believed that it was not possible to reduce road accidents effectively without knowing the ‘real causes of traffic accidents’. This opinion was expressed in the first parliamentary report on traffic safety in Norway (Ministry of Justice, Parliamentary report 83, 1961–62, On measures for promoting traffic safety), stating: ‘‘A thorough planning of measures to prevent traffic accidents is of great significance if good results are to be achieved. If planning is to be effective, it is necessary to know and analyse the problems in traffic at which the measures can be directed. It is not possible at present to implement road safety planning in a totally satisfactory way. Sufficient knowledge of the real causes of accidents is not available and as a result, the best remedies are not known either. It is usually a complex set of causes that result in traffic accidents; this makes it difficult to evaluate the importance of the individual causal elements.’’ Others have rejected the use of the concept of cause in explaining accidents (Haight 1980). Accidents are the outcome of a vastly complex random process, whose general characteristics can be modelled statistically. Some of the factors that influence the stochastic process leading to accidents are known; others will never be known.
88
The Handbook of Road Safety Measures
The logic of the argument that you need to know the causes of a problem in order to solve it seems irresistible. Yet, there is not necessarily a very close connection between the causes of the problem and its solution. To see why this is not the case, it may be instructive to consider in detail some of the approaches that have been taken to explaining road accidents and discuss their implications. Theories of accident causation – a brief chronology. The scientific study of accidents started about 100 years ago. At least since that time, theories have been proposed to answer the question: Why do accidents happen? While easily asked, this is indeed a very difficult question to answer. Useful discussions can be found in a number of books. In particular, books by the following authors are recommended: Cresswell and Froggatt (1963) Shaw and Sichel (1971) Evans (1991) Wilde (1994). Five different theories trying to explain accidents will be briefly discussed. Figure 4.3 lists the theories in chronological order and indicates the heyday periods of the various theories. Accidents as random events. Accident research started 100 years ago when Bortkiewicz published his book entitled The Law of Small Numbers (Leipzig 1898). Bortkiewicz studied the frequency of deaths from horse kicks in the Prussian army. He found that the distribution of the number of deaths per army corps per year was almost perfectly random. To describe the random process leading to accidents, he used the Poisson model. This model fitted the actual distribution of accidents very closely. Bortkiewicz’s results led to acceptance of the idea that accidents were purely random events over which humans had no control. 1900 1920 1940 1960 Accidents as random events Accident proneness theory Causal accident theory Systems theory
1980
2000
Behavioural theory
Figure 4.3: The heyday periods of various accident theories.
Part I: 4. Basic Concepts of Road Safety Research
89
Accident proneness theory. The view that accidents were purely random events was shaken during the First World War, when Greenwood and Yule (1920) discovered an abnormal concentration of accidents involving a few workers in munitions factories. These workers had far more accidents than randomness alone could explain. Greenwood and Yule proposed different statistical models to explain the observed distribution of accidents. The simplest of these models that adequately described the observed distribution of accidents was the negative binomial model. This model was based on an assumption of different initial accident liabilities. Some people were, in other words, more prone to have accidents than others. This reorientation of accident theory coincided with a surge of innovations in psychology. Psychoanalysis became widely known through the writings of Sigmund Freud. The first intelligence tests and personality tests were developed. The belief soon took hold that it was possible by means of psychological tests to identify people who were particularly prone to accidents and deny them access to the activities where they were causing accidents. This point of view was predominant in accident research from about 1920 until about 1950. The pendulum had moved from one extreme to the other. From maintaining that accidents were entirely random, the conventional wisdom now held that accidents were the fault of a few people with some sort of personality disorder. An important finding undermining accident proneness theory was made as early as 1939 by Thomas Forbes (1939). He found that most car accidents were caused by ordinary drivers. Although only 1% of the drivers were involved in 23% of all accidents during the 1931–33 period, the same 1% of drivers were only involved in 4% of all accidents during the 1934–36 period. Most accidents during the latter period involved drivers who did not have any accidents during the first period. Forbes had actually demonstrated the effects of regression to the mean, although he did not himself use that term to describe his finding. Growth of mass automobilism in the 1940s and 1950s in the United States, and the attendant growth in the number of accidents, made it clear that road accidents can happen to everybody, not just a few particularly clumsy people. It was felt that the theory of accident proneness could no longer fully explain road accidents. Causal accident theory argued that it was only by finding the real causes of accidents that successful prevention was possible, and that the real causes of accidents can only be found by studying in detail each accident, the circumstances surrounding it and the events leading to it. This approach is perhaps modelled on microbiology and its search for the causes of disease by identifying microorganisms transmitting infections or other mechanisms triggering disease. Proponents of the theory took the fact that the number
90
The Handbook of Road Safety Measures
of accidents kept rising as conclusive evidence that the established approaches to the study of accident causation had not been able to find the real causes. The in-depth case study approach ended up by concluding that accidents typically have more than one cause. It is almost never possible to identify any one of the potentially contributing causes as more decisive than the others. Human factors were found to contribute to most accidents. In particular, many in-depth studies attributed accidents to errors made by road users. The strong focus of causal accident theory on human errors led, in the 1950s, to a misplaced emphasis on trying to modify human behaviour as the only effective measure to prevent accidents. It soon became apparent that these efforts were only modestly successful. It was also soon realised that merely finding that man is fallible was not enough to prevent accidents. It was necessary to find out why human errors were made. This realisation led to transition to a new kind of accident theory. Systems theory (and epidemiological theory) emerged in the 1950s and was popular in the 1960s and 1970s. The basic proposition of systems theory is that accidents are the result of maladjustments in the interaction between the components of complex systems. According to this theory, it is not possible to pick out any part of the road transport system as more crucial than others for its successful operation. Humans, for example, err, but why do they make errors? The answer proposed by systems theory was that errors are made because the system is not adequately designed and matched to human capabilities. Systems theory sought to find the solution to accidents by modifying the technical components of the road transport system. Highway and vehicle safety engineering became important factors in this work. As a theory of accident prevention, systems theory has been more successful than any other accident theory. The improvements that have been made to the road system, traffic control and motor vehicle design have dramatically reduced the accident rate per kilometre of travel in Western motorised countries. During the last 15–20 years, it has become apparent, however, that not even systems theory can provide a fully satisfactory solution to the road accident problem. Behavioural theory. Perhaps accidents are an insoluble problem? That is suggested in Gerald Wilde’s risk homeostasis theory, which figures prominently among the various behavioural accident theories that have been proposed after 1980. This theory will be discussed more in depth in the next section. The basic idea of all the behavioural theories is that human risk assessment and human risk acceptance is a very important
Part I: 4. Basic Concepts of Road Safety Research
91
determinant of the actual number of accidents in an activity. More specifically, Wilde proposes that every society has the number of accidents it wants to have and that the only way of permanently lowering this number is by changing the target level of risk (the desired level of safety). What can be concluded from this brief survey of accident theories? Firstly, it is probably fair to say that all accident theories that have been proposed contain an element of truth. It is true that accidents are, to some extent, random events. It is also true that some people are more likely to become involved in accidents than others. It is true, indeed trivially true, that man is fallible, that errors will be made and that some of these errors will result in accidents. It is true that the more we try to adapt technical systems to human limitations, the fewer errors will be made and the fewer accidents will occur. However, no system is ever entirely foolproof and the human desire to test limits and experience the thrill of hazards cannot be suppressed. Secondly, none of the theories provide a complete scientific explanation for accidents. Each theory represents a partial perspective that offers a partial explanation. The relationship between the theories is somewhat complex, as some theories seek to explain single vehicle accidents, while other theories look for explanations of variation in the number of accidents, and yet other theories attempt to explain the overall level of safety of a system. The factors that are relevant at one of these levels of analysis may not be relevant at another level. Thirdly, almost all the theories about accident causation have been proposed as means of reducing accidents, rather than out of intellectual curiosity. The desire to have a theory, which not just explains, but also tells you how to prevent the phenomenon you are seeking to explain, has been unfortunate in many ways. Some theories have obviously had a too narrow focus. Other theories are really only frameworks of concepts, not coherent deductive systems of hypotheses and empirical propositions. The accident theories that have been proposed so far have therefore not resulted in a general theory of accident causation, which can be stated in terms of law-like propositions that form a closed deductive system. By the same token, as will be discussed in Chapter 5, it is difficult to give a coherent theoretical account of the results of road safety evaluation studies. Nature of accident causes – a statistical concept of causation. Historically speaking, the concept of cause has been applied in a deterministic sense. The cause always produces the effect, and the same cause always has the same effect. If you heat an iron rod, it will always expand, and always – to within a small measurement error – expand by the same amount if a given amount of heat is applied.
92
The Handbook of Road Safety Measures
The association between the factors that contribute to accidents and accident occurrence is irreducibly statistical. By studying accidents without having any idea of how frequently various hazards occur in traffic, no conclusions whatsoever can be drawn concerning the relative importance of factors contributing to accidents. At best, some educated guesses regarding potentially contributing factors can be done. The association between various risk factors and accidents can only be detected if we know the frequency and distribution of road user exposure to the risk factors. This is no less true in the case of human factors than it is the case of road-related risk factors or vehicle-related factors. The chief merit of in-depth accident studies is that they provide more detailed accident data than ordinary accident records do. Clearly, ordinary accident records are not sufficiently detailed for studying the role of human factors in road accidents. Besides, in-depth studies are often conducted by scientific teams or trained experts. This means that the recording of information will often be more complete and more accurate than it is in the case of ordinary accident reporting by the police. Criteria of causality for statistical associations. If it is accepted that it makes sense to use the notion of causality to refer to statistical associations between variables, the next question is how to separate those statistical associations that are causal in nature from those that are not. It is obviously not true that any statistical relationship found in a data set is causal. Much work has been put into developing criteria for assessing causality in non-experimental data. The following brief discussion is based on Elvik (2001a, 2008b). In order to claim that A causally influences B, the following conditions should ideally be present:
There should be a strong statistical relationship between the presumed cause (A) and the presumed effect (B), which is consistent in subsets of the data. If the cause can reasonably be assumed to be effective only within a certain subset of the data, effects should be found only in that subset and not outside it. The direction of causality should be clear, that is it should be clear which variable is the cause and which is the effect. The cause should precede the effect in time. If the cause manifests itself at different doses, there should be a dose–response pattern between cause and effect. As an example, the higher the blood alcohol level, the greater the increase in accident rate one would expect. The statistical relationship between cause and effect should not disappear when confounding factors are controlled. Confounding factors are all factors that could have similar effects to those of the causal factor of interest. They are, as it were, competing explanations to the relationships observed.
Part I: 4. Basic Concepts of Road Safety Research
93
The findings of the study should be supported by theory. The causal mechanism through which effects are transmitted, i.e. the process through which a cause produces its effects, should be known.
A more detailed discussion of these conditions for causal interpretations of statistical relationships can be found in Section 5.2. By critically assessing statistical relationships according to these criteria, an informed opinion about the likelihood of these relationships being causal can be formed. In this sense, the concept of causality makes sense not just for deterministic laws of nature, but also for stochastic process like those leading to road accidents.
4.4 ROAD
ACCIDENTS AS A SELF-REGULATORY PROBLEM
Theory of risk homeostasis – a general explanatory model for accidents? During the last 15–20 years, there has been a lively discussion in road safety research about different models, which are intended to explain why accidents occur and the possibilities of formulating a general theory to explain accidents. The researcher, who has gone furthest in the direction of proposing a general theory for explaining accidents, is the Canadian researcher Gerald Wilde. He has put forward the theory of risk homeostasis, which briefly stated claims that the only factor that can lead to lasting changes in the number of accidents per unit of time in the long term is changes in the strength of the desire for safety in the population. Wilde’s model has provoked an extensive international discussion (Slovic and Fischhoff 1982, Graham 1982, Lund and Zador 1984, Evans 1985, 1991, Haight 1986, McKenna 1985, 1988, Summala 1988, Howarth 1988, OECD 1990, Elvik 1991, Evans and Graham 1991, Hoyes and Glendon 1993, Underwood, Jiang and Howarth 1993, Bjørnskau 1994, Bjørnskau and Fosser 1996, Fosser, Sagberg and Sætermo 1996, Sagberg, Fosser and Sætermo 1997). A detailed discussion of this research is not undertaken here for want of space. The main points of view, on which the majority of researchers agree with Wilde’s theory of risk homeostasis, can be summarised as follows:
It is not possible to refute Wilde’s theory of risk homeostasis. If it is found that a measure does not reduce the number of accidents, this confirms the theory, showing that people adapt their behaviour to a lower level of risk so that the number of accidents remains unchanged. If the opposite is found – that the number of accidents reduces – this can be explained by stating that the target level of risk has been reduced. Thus no result would lead to rejection of the theory. The theory can be invoked to explain any finding and thus has no explanatory value.
94
The Handbook of Road Safety Measures
One may interpret Wilde’s theory as an assertion that no measure is effective. According to such an interpretation, the theory is wrong. There are many examples of road safety measures that have reduced the number of accidents and/or injury severity. Wilde does not state how the ‘target level of risk’ can best be measured and how it can be influenced. This then becomes a ‘black box’ into which anything that cannot be measured can be put.
Nonetheless, many researchers would probably agree that Wilde has identified important mechanisms in his theory and that not all road safety measures have the intended effects. There is no doubt that the strength of demand for road safety is of major importance for the success of an accident prevention programme. The stronger the desire to prevent accidents, the stronger the measures it would be acceptable to take in order to prevent accidents. Theory of behavioural adaptation and factors affecting such adaptations. Wilde’s attempt to formulate a general theory to explain accidents has not been successful. A more limited theory is the theory of behavioural adaptation or risk compensation. This theory states that road users adapt their behaviour to risk factors and road safety measures to a greater or lesser extent, but not necessarily in such a way as to fully compensate for the risk factors or measures, which trigger the behavioural adaptation, as Wilde maintains. The logic in this theory is shown in Figure 4.4. It is assumed that every road safety measure is intended to affect accidents by affecting one or more of the risk factors, which contribute to accidents or injury severity (risk factors that the measure is meant to affect). In addition to these factors, a road safety measure may have unintentional effects on one or more other risk factors, which affect accidents or injury severity. If these risk factors are adversely affected, this can partly or fully outweigh the favourable effects on the risk factors, which the measure is designed to affect. It is these compensatory changes in other risk factors, rather than those that the measure is primarily designed to affect, which are referred to as risk Risk factor to be influenced Road safety measure
Changes in number of accidents Other risk factors
Figure 4.4: The logic of the theory of behavioural adaptation (risk compensation).
Part I: 4. Basic Concepts of Road Safety Research
95
compensation. The conceptual scheme, which is used as a basis for Figure 4.4, has been developed by Evans (1985, 1991). Let us illustrate this model by means of an example. Road lighting is intended to affect accidents by making it easier to see other road users and objects in the dark. The detection distance in the dark is the risk factor that road lighting is meant to influence. The effect road lighting has on accidents if the detection distance alone is increased, and road users do not change their behaviour, is referred to as the engineering effect of the measure. The engineering effect is shown by the uppermost arrows in Figure 4.4. Suppose that road lighting also leads to road users driving faster and reducing their alertness. Such behavioural changes are not intended by the road authorities and can lead to road lighting having a smaller effect on accidents than it would otherwise have had. The behavioural changes can be labelled the behavioural effect of the measure and go through the lowest row of arrows in Figure 4.4. The net effect of a measure is determined by the engineering effect and the behavioural effect, and the direction and strength of these effects. Much of the research into behavioural adaptation in traffic has tried to find out why behavioural adaptation occurs in some cases and not in others, and to describe better the forms that behavioural adaptation can take. One form of behavioural adaptation, which is probably important, but which is very difficult to study, is the change in alertness among road users. Lower alertness is not necessarily easily observable. For example, reduced alertness does not necessarily lead to a change in speed. Some of the factors that influence the likelihood of behavioural adaptations include (Bjørnskau 1994, Elvik 2004):
Noticeability of the measure: Measures that lead to noticeable improvements, which road users believe reduce the risk of accidents are more liable to risk compensation (behavioural adaptation) than measures that do not lead to noticeable improvements. Example: Road markings are assumed to be more liable to behavioural adaptation than collapsible steering columns. Whether the measure reduces accidents or injuries: Measures that reduce the risk of accidents are more liable to risk compensation than measures that reduce the severity of injuries in accidents. Example: Airbag system (ABS) is assumed to be more liable to behavioural adaptation than airbags. Whether road users have already compensated for the risk factors that the measure is meant to influence or not: If road users have already adapted their behaviour to the risk factor that the measure is meant to affect, the measure is more liable to risk compensation than if such an adaptation has not taken place. Example: Periodic inspections of private cars must be assumed to be more liable to behavioural
96
The Handbook of Road Safety Measures
adaptation than road lighting, because road users try to compensate for technical defects so that the accident rate does not increase, but they do not adapt their behaviour in the dark in such a way that the increase in risk in the dark disappears. The size of the engineering effect: The greater the engineering effect, the greater the probability that there will be behavioural adaptation. Example: It is more likely that improving a car’s headlights will lead to behavioural adaptation when driving in the dark than when driving in daylight. The benefits of changing behaviour: A measure can only lead to behavioural adaptation if road users experience some benefit in changing behaviour. Example: It is difficult to think of a behavioural adaptation to gates at level crossings between roads and railroads, which would increase the benefit to road users. Driving in a zigzag pattern between gates that are lowered is extremely dangerous and may also lead to damage to the vehicle if one hits the barriers. The vast majority would hardly perceive this as either beneficial or comfortable. Reducing alertness, so that one reacts later confers no benefit either, because it only means that one would have to brake harder in order to stop in front of the gate.
Can behavioural adaptation explain why some road safety measures do not seem to reduce accidents? Among the road safety measures that are included in this book are both measures that, according to the studies available, reduce the number of accidents, and measures that do not do so. The latter group includes tracks for walking and cycling, resurfacing of roads, bright road surfaces, and basic driver training. Can behavioural adaptation among road users explain why these measures, and others, do not lead to fewer accidents? The answer to this question is yes in the majority of cases, but with a number of reservations. Obviously, it can always be claimed that behavioural adaptation explains the lack of effect on accidents of a measure. However, very often, such behavioural adaptation is not fully documented. Unfortunately, convincing evidence of changed behaviour is only rarely found. For example, no one has shown that the ineffectiveness of edge lines in reducing accidents is due to behavioural adaptation among road users. We cannot rule it out, but we do not know if behavioural adaptation is the reason why no reduction in accidents has been found. Behavioural adaptation does not necessarily fully eliminate the effect of a measure on accidents. For example, road lighting reduces the number of injury accidents in darkness by around 30%. This is a major effect. Few would believe that this measure leads to behavioural adaptation. Figure 4.5 shows the results of a study of drivers’ speed adaptation on a road section where road lighting was installed.
Part I: 4. Basic Concepts of Road Safety Research
97
Changes in mean speed on straight sections and in curves after road lighting was installed (Bjørnskau and Fosser, 1996) 84 81.4
Mean speed (km/h)
82 80 78
78.0
79.0 77.8
76 74
Before After
72.1
72
70.8 71.3
70.3
70 68 66 64 Straight Daylight
Curve Daylight
Straight Darkness
Curve Darkness
Figure 4.5: Changes in mean speed following the introduction of road lighting (Bjørnskau and Fosser 1996). 4.5 shows the average speed in kilometres on straight roads and in curves, in daylight and the dark, before and after road lighting was introduced. Speed increased in the dark, especially on straight roads. If the changes in speed from the before to the after periods in daylight is used as a comparison group, the net increase in speed in the dark can be estimated to 3%, both on straight roads and in curves. The study also found that drivers were less attentive on lit roads than on unlit roads. The increase in the detection distance to given objects in the dark, measured under controlled conditions, can be used as a measure of the engineering effect of road lighting. According to Ketvirtis (1977), the detection distance increases from a maximum of 50 to 75 m with correctly installed vehicle lights as the only source of light, to around 250 m when the road has road lighting of the standard required for national highways in Norway (light intensity of 1–2 cd/sqm). At a driving speed of 78 km/h, with a 1 s reaction time and a friction coefficient of 0.8, the stopping distance is about 52 m. Road lighting thus provided an increase in the safety margin from 75 – 52 ¼ 23 m before it was installed to 250 – 52 ¼ 198 m after it was installed. The engineering effect of road lighting, in other words, corresponds to a potential decrease in accidents in the dark of at least 80%. The actual decrease in accidents is around 30%. This indicates that road lighting leads to significant behavioural adaptation, which contributes to reducing, but not eliminating, the effect on accidents.
98
The Handbook of Road Safety Measures
Nonetheless, it is rarely possible to estimate how behavioural adaptation modifies the effects of a measure on accidents in this way. Firstly, the engineering effect of many measures is not known or is impossible to calculate. Secondly, the incidence of behavioural adaptation is rarely known. Thirdly, it is not known, even with regard to road lighting, which form of behavioural adaptation has the greater effect. Is it increased speed or reduced alertness? The only effect that one can expect to quantify is, at best, the total effect of all forms of behavioural adaptation, and not the partial contributions of the many forms such adaptation may take.
5.
A SSESSING THE Q UALITY E VALUATION S TUDIES 5.1 THE
OF
CONCEPT OF STUDY QUALITY
Study quality is used synonymously with study validity. Validity denotes the degree to which research approximates the truth. This definition is taken from Cook and Campbell (1979). The words ‘approximates the truth’ in the definition are used deliberately, since researchers can never claim to know the truth for sure. The best that can be accomplished in empirical social research is to conduct studies in ways that are not known to lead to systematic errors and to argue on that basis that the results are not (positively) known to deviate from the truth. This, however, is not the same as to claim that the truth has been found. Study quality is affected by a large number of characteristics of study design and conduct. The various aspects of study quality will be referred to as types of validity and are discussed in greater detail below.
5.2 ASSESSING
STUDY QUALITY
Study quality is assessed by making a systematic evaluation of the validity of a study or a set of studies in terms of specific criteria of study validity. Ideally speaking, an assessment of study quality ought be standardised and expressed in numerical terms by means of a scale for study quality. So far, however, no formal scoring system for assessing the quality of road safety evaluation studies has been developed. The
The Handbook of Road Safety Measures Copyright r 2009 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISBN: 978-1-84855-250-0
100
The Handbook of Road Safety Measures
assessment of study quality that has been done in this book is systematic, in the sense that it relies on explicitly stated criteria of study quality that are applied consistently to all studies. It is, however, not numerical, that is, the results of the assessment are not summarised quantitatively. Research is ongoing with the aim of developing a quantified study quality score. The task has turned out to be more difficult than originally believed. Study quality comes in degrees. It is not the case that studies are either perfect or worthless. Most often, if a study is checked systematically according to a list of criteria for study quality, it will be found that the study satisfies some of these criteria, but not all of them. The question then arises: Can we trust the findings of a study that is known to have some shortcomings? Are some shortcomings more important than others? It is not always simple to answer these questions. The answers given in this book are based on what past experience has taught us about the shortcomings that most strongly affect the results of road safety evaluation studies. A certain weakness found in a study is important if it is known that it may strongly influence the results of a study. It is, on the other hand, rather less important if it is known not to have an impact on study findings. Criteria of study quality. The criteria of study quality that have been applied to assess the road safety evaluation studies referred to in this book, are to a great extent based on the validity framework of Cook and Campbell (1979). According to this framework, the quality of a study can be assessed in terms of four types of validity:
Statistical conclusion validity: Sampling techniques, statistical analyses Theoretical validity: Operational definition of theoretical concepts and propositions External validity: Generalisability of the results of a study Internal validity: Basis for inferring a causal relationship between treatment and effect.
Statistical conclusion validity refers to the accurateness and the representativeness of the data and the results of statistical analyses in a study. Study results are statistically valid if they cannot be attributed to randomness or bias of the measurements and if they are representative of a known population of units. The statistical conclusion validity is assessed in terms of the following criteria, Sampling technique: The best way of sampling study units is by means of random sampling from a known sampling frame. Random sampling ensures that there is no systematic bias in the sample. However, a sampling frame from which random sampling of study units can be made does not always exist. In that case, other sampling
Part I: 5. Assessing the Quality of Evaluation Studies
101
techniques are employed. In road safety evaluation studies, samples defined according to warrants or criteria for the use of a road safety measure and convenience samples (or self-selected samples) are common. Sample size: Sample size refers to the number of study units included in a study. Larger sample sizes are associated with smaller statistical uncertainty of the results. In most road safety evaluation studies, sample size refers to the number of accidents. However, in some studies, data refer to aggregates of elementary study units, for example, to numbers of accidents per state per month or per year. This involves the risk of an error of aggregation, and the statistical validity of such studies may be reduced. An example of this was given in Section 4.2, in the discussion of accident rates for men and women. Reporting of statistical uncertainty in results: In order to assess the statistical uncertainty of the results from a study, the sample size, or alternatively, the standard error (or information sufficient to derive the standard error) has to be reported. Studies that fail to report this information cannot be included in meta-analysis. Measurement errors: Measurement errors may contribute to systematic bias, which may lead to a systematic over- or underestimation of the effects of a measure. An example of measurement errors in road safety studies is under-reporting of accidents. Specification of accident or injury severity: Studies that specify the severity of the accidents or injuries to which results apply are rated as better than studies that do not specify accident or injury severity. Firstly, the effects of many road safety measures have been found to vary, depending on accident severity. Secondly, fatalities and severe injuries are regarded as a more serious problem than minor injuries or accidents that result in property damage only. Theoretical validity, or construct validity, refers to the theoretical foundation and the operational definition of theoretical concepts and propositions. Criteria for theoretical validity are as follows. Identification of relevant concepts and variables: Relevant concepts and variables and how they can best be measured are specified. Relevant variables may be independent, dependent, confounding, mediating and moderator variables (Figure 5.1). Mediator variables are variables that are affected by the independent variable and that have an effect on the dependent variable. Moderator variables are variables that affect the relationship between independent and dependent variable, that is, the relationship differs between different groups or levels of the moderator variable. Confounding variables are variables that are related to both independent and dependent variable, and whose relationship to the dependent variable may be mistaken for an effect of the
102
The Handbook of Road Safety Measures
Confounding Moderator Dependent
Independent Mediator
Figure 5.1: Relationships between relevant study variables. independent variable. Confounding variables may also be uncontrolled moderator variables. Hypotheses describing the relationships between variables: Hypotheses are formulated on the relationships between those variables that are assumed to be relevant, based on an explicitly stated theoretical background. The hypotheses contain assumptions about the direction and strength of the relationships between the variables. Additionally, the most important alternative hypotheses are identified, which may explain study findings if the proposed theory is contradicted. Knowledge of causal mechanism: If a study can identify the causal mechanism through which a road safety measure influences accidents or injuries, the theoretical validity is strengthened, provided observations of the causal mechanism make sense. It would, for example, make sense if a study found that a reduced speed limit was associated with lower speeds and less serious accidents. If, on the other hand, speed was reduced but accidents became more serious, it would be more difficult to accept this finding as showing the true effect of reduced speed on accident severity. Unfortunately, most of the road safety evaluation research does not rely on an explicitly stated theoretical foundation. Some studies test explicitly stated hypotheses, but the hypotheses are rarely based on a well-established theory. This is one of the major problems of this research, because it means that few results can be ruled out as nonsensical on theoretical grounds. If you heat an iron rod, and it does not expand, you can rule out the possibility that you have made an important new discovery in physics. It is far more likely that there is something wrong with your experiment or measurements. If road lighting were installed, could we rule out the possibility that the number of accidents in the dark would increase? Not really. While common sense and general knowledge about visibility at night lead us to expect that safety will be improved, the possibility of an opposite result cannot be ruled out. If road users take advantage of road lighting by driving faster, by driving more at night, or by driving when they are more tired or less attentive, there may be no benefits for road safety.
Part I: 5. Assessing the Quality of Evaluation Studies
103
External validity refers to the generalisability of study results. A study has high external validity if its findings are valid for different settings than those in which the study was made. It is difficult to assess the external validity of a single study. External validity is best assessed by comparing the findings of studies that have been made in different settings. If the findings are similar, the external validity of the set of studies is high. If findings differ greatly, external validity is more doubtful. However, context-specific effects of safety measures are not necessarily a methodological weakness, but rather a property of reality. Nevertheless, the generalisiability of context-specific effects is reduced. To some extent, high external validity can make up for the absence of a strong theoretical foundation for road safety evaluation studies. Results that have been replicated a large number of times, in many studies made in many countries, are more likely to show true effects than results reported by just a few studies in just a few countries. Internal validity refers to the inference of a causal relationship between treatment and effect. This aspect of study validity is very important. The objective of a road safety evaluation study is to determine the effects on safety of a road safety measure. To measure the effects of something is the same as describing the causal relationship between the action taken and the associated changes in road safety. Criteria for causality were discussed in Section 4.3, but are worth repeating here. The following criteria indicate that there is a causal relationship between a safety measure A and a safety indicator B (Elvik 2008a): Statistical association between treatment and effect: There is a (strong) statistical relationship between A and B, which is consistent between different data sets or subgroups, and which is found only within the target group for measure A. The strength of the relationship is assessed both in terms of the size of the effect and in terms of statistical significance or confidence intervals. When different effects are found within different subgroups, these should be predicted from hypotheses proposed before the study. If a statistical relationship is inconsistent, and no moderator variable can be found to account for this, it weakens a causal inference. Clear direction of causality: The direction of the relationship can be explained in both theoretical and empirical terms. Causal direction can be determined if there is a clear temporal relationship between the variables (the cause comes before the effect), and there should be a known mechanism that explains the effect of A on B. It should be ruled out that A is caused by B. Dose–response pattern: If a treatment comes in different doses, one should expect to find a dose–response pattern. The larger the dose, the greater the effect. If this is found, it strengthens a causal inference.
104
The Handbook of Road Safety Measures
Specificity of effect: It is sometimes possible to identify a certain group of road users, a certain type of accident, or some other category in which one would expect a treatment to be effective. If the treatment is found to be effective in the ‘target group’, but not in other groups, then internal validity is strengthened. A limitation of this criterion is that it is sometimes difficult to define the target group for a measure very precisely (Hauer 1997). Control of confounding factors: The relationship between A and B does not disappear when potential confounding variables are controlled for. Confounding variables are all variables that are related to both A and B, when the relationship of these variables with B can be mistaken for an effect of A on B. Examples of confounding variables are other safety measures that are implemented at the same time as A, or general trends in accident numbers. When confounding variables are not controlled for, the effects of measure A are often systematically over- or underestimated. When effects of other safety measures or accident trends are not controlled for, the effects of measure A will most likely be overestimated. Adequate control of confounding variables is perhaps the single most important aspect of study quality for road safety evaluation studies. Lack of control for confounding variables can profoundly influence the results of an evaluation study, as will be shown by some examples in the next section. Studies may control for confounding variables in two ways. Firstly, the study design contributes to the degree to which confounding variables are controlled for. The best way of controlling for confounding variables by study design is randomisation, that is, each unit of the whole population has the same probability of being selected for the treatment group (in which a measure is applied) or for the control group (in which no measure is applied). When possible confounding variables are known, matching may be an alternative to randomization, that is, study units with and without the application of a measure are compared pairwise, each pair being equal with respect to confounding variables. A study may also be restricted to study units, which are identical with respect to confounding variables. In this case, the generalisability of the results may be limited. Secondly, control for confounding variables may be achieved by statistical techniques. In multivariate analysis, potential confounding variables can be included as predictor variables whereby the effects of those confounding variables that are included in the analysis are statistically controlled for. However, multivariate analyses are no guarantee for internal validity.
Part I: 5. Assessing the Quality of Evaluation Studies
105
In the following, the most common factors that reduce the internal validity of studies are summarised.
Regression to the mean: It means that accident numbers that have been exceptionally high or low at one time are most likely to be closer to the mean at a later time. Regression to the mean is often a problem in before-and-after studies in which measures are applied only in cases (e.g. on roads) where the number of accidents has been exceptionally high. Without control for regression to the mean, the number of accidents will decrease in the after-period even if the measure has no effect at all. How regression to the mean can be treated statistically has been described in Section 4.1. Self-selection bias: There are often systematic differences between persons (or other study units) who choose to apply a measure on a voluntary basis and those who do not. If self-selection bias is not controlled for, the effects of a measure will almost always be overestimated. Accident migration: Safety measures may reduce accidents in those places or at those times where/when the measures are implemented. In other places or at other times, accidents may increase. Study effects: The fact that a study is conducted or that measurements are made may have an influence on those aspects that are measured in a study (e.g. if speed measurement devices are not hidden for drivers, drivers may reduce speed because of the measurement equipment).
In multivariate analyses, there are a number of further possible sources of error that may reduce the internal validity of the study results (Elvik 2008a):
Endogeneity: When a measure is used only where there have been many accidents, the number of accidents may remain higher than elsewhere after the measure has been applied, even if the number of accidents actually has been reduced over time. Incorrect functional form of the independent variable: When a functional form of the independent variable is used in a multivariate model that does not correspond to the relationship of this variable to the dependent variable, the relationship that is found between the independent and dependent variable will be weaker than when a more adequate functional form is chosen (e.g. if a linear function is applied when a U-shaped relationship exists). Collinearity and omitted variables: Results from multivariate analysis may be biased if too many predictor variables are included in the model, and if relevant predictor variables are not included in the model. It is a problem that one never knows for sure that all relevant variables, but not more, are included in a model.
106
The Handbook of Road Safety Measures
5.3 THE
IMPORTANCE OF STUDY QUALITY:
SOME
ILLUSTRATIONS
This section will give some examples of how various aspects of study quality can influence study findings. Case 1: Errors in data and their correction. Road safety evaluation studies are sometimes based on data that may contain systematic errors. A case in point is studies that evaluate the effects of seat belts on injury severity. The results of studies that evaluate the effect of seatbelts can be strongly influenced by how reliable the information is regarding the use of seat belts and injury severity. People who are uninjured or slightly injured will often have left the vehicle before the police arrive at the accident scene. When the police ask whether belts were used or not, a number of those who were not using a seatbelt will say that they were using a seat belt, in order to avoid problems. In this way, the use of seatbelts may be systematically over-reported among uninjured and slightly injured people involved in traffic accidents. A study that took this source of error into account was carried out by Dean, Reading and Nechodom (1995). Table 5.1 shows the results of the study, with and without adjustment for possible over-reporting of seat belt use in accidents. Dean, Reading and Nechodom estimated the actual use of seat belts in accidents on the basis of information on seat belt use in traffic and an assumption that seat belt use was correctly stated for fatal injuries. The way correct seat belt use was estimated is of course subject to debate. The main point here is to show that errors in data can have a major impact on the estimates of effect reported in an evaluation study. Case 2: Misleading statistical analysis of data. Lyles, Lighthizer, Drakopoulos and Woods (1986) report a before-and-after study of jurisdiction-wide upgrading of traffic control devices in cities in Michigan. They conclude that: ‘‘Results of assessing the Table 5.1: Estimated effect of seat belts on the probability of different injuries in accidents, depending on adjustment for over-reporting of seat belt use in accidents (Dean et al. 1995) Percentage change in number of injuries attributed to seat belts Injury severity
Stated seat belt use
Adjusted seat belt use
Killed
85
54
Seriously injured
80
49
Slightly injured
52
25
All injured
55
28
Part I: 5. Assessing the Quality of Evaluation Studies
107
overall effectiveness of traffic control device upgrading on a jurisdiction-wide basis were mixed at best.’’ Tables 1 and 2 of the paper report changes in the distribution of accidents by type in all cities that were included, except for Pontiac, which was by far the largest of the cities where traffic control devices were upgraded. Based on the analyses of the data in these two tables, the authors conclude that: ‘‘The overall results are not particularly enlightening in terms of the effects of the Traffic Control Device upgrading.’’ Table 3 of the paper presents the total number of accidents before and after upgrading for treated and untreated roads in seven cities. The authors, however, concentrate more on changes in the distribution of accidents by type and conclude that: ‘‘The results are inconsistent in general.’’ Table 5.2 reproduces the data presented in Table 3 of the original paper. Table 5.2 shows that the effects of upgrading traffic control devices do indeed seem to be inconsistent, as stated by the authors. The estimate of effect in each city ranges from about 21% accident reduction to about 15% increase in the number of accidents. However, five of the seven estimates indicate a reduction in the number of accidents. If the seven estimates of effect are combined by means of a fixed-effects log odds metaanalysis, the summary estimate of effect is a 9% reduction in the number of accidents, with a 95% confidence interval ranging from 14% to 3% accident reduction. A test of the homogeneity of the seven estimates of effect shows that these estimates do not differ significantly. Combining them by means of a fixed-effects model is therefore correct. If, however, Pontiac is omitted, the summary estimate of effect becomes a 4% accident reduction, with a 95% confidence interval and 14% reduction to 7% increase. Table 5.2: Changes in the number of accidents associated with upgrading of traffic control devices (Lyles et al. 1986, Table 3) Treated roads City
Comparison roads
Before
After
Before
After
Estimate of effect (odds ratio)
304
161
256
171
0.793
44
41
109
88
1.154
East Tawas
115
83
79
58
0.983
Hudsonville
122
108
74
67
0.978
69
44
44
27
1.039
Albion Dundee
Mackinaw City Mt Pleasant Pontiac
727
747
876
913
0.986
4483
4019
3106
3104
0.897
108
The Handbook of Road Safety Measures
In short, the conclusions drawn by the authors of this study are misleading, because
the authors focussed on changes in the distribution of accidents by type, rather than the total number of accidents; the authors incorrectly state that there was no overall effect on safety; and the authors incorrectly claim that the effects were inconsistent, suggesting that there was more than random variation in effects between cities.
Case 3: How approaches taken to the analysis of data can influence results? Little (1971) has published a paper aptly titled ‘‘Uncertainties in evaluating periodic motor vehicle inspection by death rates.’’ In the paper, Little tries to determine the effect of periodic motor vehicle inspection on population death rates from road accidents in American states. Excerpts of the data used for this purpose are shown in Table 5.3. Based on these data, several estimates of the effect of periodic motor vehicle inspection can be generated (Table 5.4). If one simply compares the fatality rate before and after periodic motor vehicle inspection was introduced in the treated states, a 10% increase is observed. If these states are compared with comparison group 1, a 5% increase of fatality rate can be estimated. The following estimates of the effects of periodic motor vehicle inspection on population fatality rate can be derived from the information given in Table 5.3. Estimates ranging from a 25% reduction of fatality rate to a 10% increase of fatality rate can be generated merely by varying the cells of the data table, which are included in an analysis. Which of these estimates is the best one? An estimate that utilises as much of the available data as possible would often be regarded as the best one. In this case, that would be the before-and-after estimate, using comparison group 3. This estimate utilises data from all the states, rather than just a particular group of states. Table 5.3: Data for evaluating the effects of periodic motor vehicle inspection on population death rates Road accident fatalities per 100,000 inhabitants per year Group of states
Before
After
Treated group: States that introduced periodic inspection
25.88
28.52
Comparison group 1: States that had periodic inspection all the time
17.89
18.74
Comparison group 2: States that did not have periodic inspection
22.28
23.29
Comparison group 3: All other U.S. states
22.63
23.06
Source: Adapted from Little (1971).
Part I: 5. Assessing the Quality of Evaluation Studies
109
Table 5.4: Estimates of effects of periodic motor vehicle inspection on population death rates, based on the results in Table 5.3 Way of analysing data Simple before-and-after in treated group
Effect attributed to periodic motor vehicle inspection (%) þ10
Before-and-after using comparison group 1
þ5
Before-and-after using comparison group 2
þ5
Before-and-after using comparison group 3
þ8
Comparison of states with and without inspection–before (comparison group 1 compared to comparison group 2)
20
Comparison of states with and without inspection–after (comparison group 1 compared to comparison group 2)
20
Comparison of states with and without inspection–before (comparison group 1 compared to treated groupþcomparison group 2)
25
Comparison of states with and without inspection–after (comparison group 1 compared to treated groupþcomparison group 2)
2
Case 4: Inadequate control of confounding factors – black spot treatment. It has repeatedly been found that the effects attributed to a measure or programme in evaluation studies depend on the quality of those studies. In fact, this has been found so often that Rossi and Freeman, as early as 1985, launched ‘the Iron Law of Evaluation Studies’, which states ‘‘The better an evaluation study is technically, the less likely it is to show positive program effects.’’ (p. 391) In a paper published in Accident Analysis and Prevention in 1997 (Elvik 1997, reprinted in Elvik 1999), an attempt was made to test the Iron Law of Evaluation Studies by using studies that have evaluated the effects on accidents of black spot treatment as a case. Studies were classified according to whether or not they controlled for regression to the mean, changes in traffic volume, long-term trends in numbers of accidents and accident migration. The classification of studies was generous: studies that claimed to have controlled for any of the confounding factors were treated as having done so, although some studies did not explain in sufficient detail how they had controlled for the confounding factors. Figure 5.2 gives a sample of the results of the study. It shows the percentage change in the number of injury accidents attributed to black spot treatment, depending on which confounding factors studies controlled for. In simple before-and-after studies that did not control for any of the four confounding factors, an impressive accident reduction of 55% was attributed to black spot treatment. In studies that controlled for regression to the mean, long-term trends and
110
The Handbook of Road Safety Measures
Confounding factors controlled for in evaluations of black spot treatment One
None
Traffic volume
Two
Regression to the mean Trend
Three
Trend. Trend. Trend. Regression. Trend. Volume Regression Migration Migration
Percent change in the number of injury accidents
0 -2
0
-10 -20
-17
-30
-27
-26 -33
-40
-39
-50 -60
-55
Figure 5.2: The importance of confounding factors in before-and-after studies of black spot treatment (Elvik 1997).
accident migration, the effect attributed to black spot treatment was zero. There is clear tendency in support of the Iron Law of Evaluation Studies: The more the confounding factors a study controlled for, the smaller the effects attributed to black spot treatment. Now, some people might wonder how we can know that a potentially confounding factor, say long-term trends, actually did confound a study. The answer is simple. If the effect attributed to the road safety measure differs depending on whether or not the potentially confounding factor is controlled for, then it does in fact confound study results. Potentially confounding factors do not, of course, always actually confound the results of a study. If there are no long-term trends in accidents, then this factor cannot confound. The point is that we cannot know whether or not a potentially confounding factor actually confounds a study unless we control for it. The fact that a certain factor is potentially confounding is, in other words, a sufficient condition for trying to control for it. Only an experimental study design in which units are assigned randomly to a treated and untreated group makes sure all potentially confounding factors are controlled for. In non-experimental studies, the best we can do is to control for the confounding factors that are known at any time, and for which relevant data can be obtained.
Part I: 5. Assessing the Quality of Evaluation Studies
111
Let us return for a moment to Figure 5.2. It has been claimed that: ‘‘considerable safety benefits may accrue from application of appropriate road engineering or traffic management measures at hazardous road locations. Results from such applications at ‘black spots’ demonstrating high returns from relatively low cost measures have been reported worldwide.’’ (quoted from Elvik 1997). Is this claim justified? The pattern shown in Figure 5.2 would seem to support a rather harsh verdict: The claim that black spot treatment is an effective way of preventing road accidents is totally unsubstantiated. It is based on an uncritical acceptance of studies that must be rejected because they did not control for important, and well-known, confounding factors. Case 5: Confounding factors are important for study findings. Another illustration of the importance of confounding factors in before-and-after studies is based on a number of evaluations of road safety measures in Norway. Figure 5.3 presents the key findings of this study (Elvik 2002). Seven different road safety measures were evaluated, all by means of before-and-after studies. For some of the measures, more than one evaluation study has been reported. Each study controlled for regression to the mean and general trends in a larger area in which study sites were located. Controls for these confounding factors were introduced in such a way that it was easy to remove them, thus producing the results a simple before-and-after study would have yielded.
Estimated percent change of the number of accidents
0
Traffic Bypass separation roads
-10
New Lane add. Black Horizontal urban and spot curve Speed arterials median treat . treatment cameras
-6
-9
-13 -20
-17
-18 -19
-16
-30
-19 -24
-32 -40 -50
-44 -49
-47 -52
-60 Not controlled for regression-to-mean and general trends Controlled for regression-to-mean and general trends
Figure 5.3: Comparison of controlled and uncontrolled estimates of the effects of seven road safety measures evaluated in Norway (Elvik 2002).
112
The Handbook of Road Safety Measures
As can be seen from Figure 5.3, the effects attributed to the seven road safety measures were almost always greater, in some cases substantially greater, when no confounding factors were controlled for, than when the effects of the confounding factors were removed. On the average, a 32% accident reduction was found in the uncontrolled studies. The studies that controlled for regression to the mean and general trends found a 16% accident reduction. Once more, there was support for the Iron Law of Evaluation Studies. Countless such examples could be given. There are so many examples of poor studies that the really good ones are to be highly valued. Case 6: Ambiguous direction of causality. It is common to evaluate the effects of safety measures by comparing accident rate between groups (or roads, drivers or vehicles) where the measure is applied or not. The results of such comparisons will be absurd if the implementation of measures depends on the accident rate. An example is the relationship between speed and accidents (Elvik 2008a). Figure 5.4 shows accident rates on roads with different average speed. At first sight, higher speed seems to be related to reduced accident rates. This does not mean that higher speed reduces accident rates – only that speed limits are lower on roads with higher accident rates. When only roads with the same speed limit are taken into consideration (the four groups of data points, each of which are roads with an identical speed limit), it can be seen that accident rate increases with increasing speed.
Injury accidents per 100 million vehicle kilometres
100
80
60
40
20
y = -0.9692x + 101.55 R2 = 0.8205
0 0
20
40
60
80
100
Mean speed of traffic (km/h)
Figure 5.4: Simple bivariate relationship between the mean speed of traffic and injury accident rate (Elvik 2008a).
Part I: 5. Assessing the Quality of Evaluation Studies
5.4 THE
113
TREATMENT OF STUDY QUALITY IN META-ANALYSIS
How has study quality been treated in the meta-analyses reported in this book? Briefly stated, study quality has been assessed in terms of the criteria stated in Section 5.2, the influence of study quality on the outcome of studies has been investigated, and as far as possible the results from the best studies are presented. The main criterion for assessing study quality was how well a study controlled for confounding factors. In order to assess the influence of study quality on the outcomes, the available studies have been grouped according to study quality. Then differences between – otherwise comparable – results from studies of varying quality are identified, or the effect of study quality is investigated with meta-regression analysis. When significant differences are found, that is, if results from studies are affected by study quality, the findings of the best studies only are presented. In some cases, all available studies are rather weak. In such cases, comments are given to alert the reader to the possibility that the study findings quoted could be misleading. It cannot be concluded that such study findings are positively wrong or misleading, only that the studies quoted did not control very well for confounding factors, and that a causal inference is therefore not possible. The amount and quality of evaluation studies vary substantially from one road safety measure to another.
5.5 CAN
THE FINDINGS OF ROAD SAFETY EVALUATION STUDIES BE ACCOUNTED
FOR IN THEORETICAL TERMS?
As already noted, one of the major problems of road safety evaluation research is the fact that most of this research does not have a strong theoretical basis, which guides the design of studies and the interpretation of study findings. The lack of a strong theoretical basis for research means that few results of road safety evaluation studies can be ruled out on theoretical grounds. Results of road safety evaluation studies that initially strike us as counter-intuitive can usually be given some ad hoc and post hoc explanation, but could almost never have been predicted in advance based on law like relationships or other precise theoretical notions. In general, there are two ways to interpret the findings of empirical studies:
Substantive interpretations, which, ideally speaking, offer a validated explanation of study findings in theoretical terms, for example, by referring to causal relationships.
114
The Handbook of Road Safety Measures
Methodological interpretations, which usually amount to rejecting the findings of a study or set of studies because the studies relied on poor data or employed flawed research methods.
Ideally speaking, researchers want to do research in ways that rule out methodological explanations of study findings. Regrettably, road safety evaluation research does not have a strong theoretical foundation, or a strong tradition for using experimental study designs. Most of this research does not refer explicitly to any theoretical framework at all, and often relies on relatively weak, non-experimental study designs that make it impossible to rule out methodological interpretations of the findings. The prospects of giving a coherent theoretical account of the findings of such studies would therefore seem to be bleak. Although the findings of road safety evaluation studies do not conform closely to a set of law-like statements, some quite general concepts could be imagined that could be used in trying to discern patterns in the finding of these studies that would lend some credibility to them. Such concepts include:
Complexity: The amount of new information a road user has to process per unit of time. When complexity is high, road users have to pay attention to rapidly changing traffic situations, in addition to performing the usual perceptual-motor tasks of walking, cycling or driving a motor vehicle. Compatibility: The differences between categories of road users in terms of the kinetic energy produced by their movements. The smaller these differences, the more compatible are road users. Energy: Kinetic energy that is converted to others forms, such as deformation, in case of accident. Kinetic energy is a function of speed and mass. Predictability: The reliability with which the behaviour of a road user can be predicted in a given situation. Lane-keeping is an example of very predictable behaviour. Visibility: The possibility of seeing something at a distance. The greater the distance at which an object can be seen and identified, the greater the visibility. Individual rationality: Choice of the best means to realise given ends. It might be assumed that road users do not want to become involved in an accident. Hence, an accident can always be treated as a breakdown of rationality.
On the basis of these concepts, the following hypotheses could be developed regarding the effects of road safety measures. Road safety will normally be improved when
complexity is reduced, incompatible road users are separated,
Part I: 5. Assessing the Quality of Evaluation Studies
115
the amount of energy released in accidents is reduced, road user behaviour is more predictable, visibility is enhanced, and incentives road users get for safe behaviour are strengthened.
To the extent that the findings of road safety evaluation studies conform to predictions based on these hypotheses, it could be said that the overall pattern of these results makes sense. Let us consider a few examples of findings in evaluation studies that can be interpreted on the basis of the six hypotheses listed above. Motorways (freeways) are, in many ways a very simple road system from which at least some of the complexities found in other types of traffic environment have been removed (Hypothesis 1). There are no access points to adjacent properties. There are no at-grade junctions. Pedestrians, cyclists and slow-moving motor vehicles are not allowed to use motorways. The road surface is generally kept in a good state. There are no sharp and surprising curves along the road. Oncoming traffic is separated by means of the median, which often has guardrails. In view of these design features of motorways, one would expect the accident rate to be lower than it is in more complex traffic environments – as is indeed the case. Road lighting and daytime running lights both enhance visibility and reduce accidents (Hypothesis 5). Seat belts and crash helmets reduce injury severity (Hypothesis 3). Rewarding safe driving has been found to reduce accident rates (Hypothesis 6). Many more examples could be given of findings that are explicable in terms of the hypotheses listed above. Yet, this does not amount to much by way of giving a theoretical account of the findings of road safety evaluation studies. The six hypotheses proposed do not represent a well-established body of theory: six different hypotheses could easily have been proposed in order to account for the findings of road safety evaluation studies. Besides, giving examples of study findings that are supported by the hypotheses does not rule out the possibility of alternative, methodological interpretations of the same study findings. Undertaking a rigorous test of whether what we know from road safety evaluation research makes sense from a theoretical point of view is a major research project that is beyond the scope of this book.
6.
T HE C ONTRIBUTION OF R ESEARCH S AFETY P OLICY -M AKING 6.1 AN
TO
R OAD
IDEALISED MODEL OF THE POLICY-MAKING PROCESS
An analytical model of the policy-making process is presented in Figure 6.1 as a starting point for discussing the nature of the contribution research can make to road safety policy-making. This model is used as a heuristic device only. It is not meant to be a literally correct description of how road safety policy-making actually proceeds. The stages identified are listed in logical order, but in actual policy-making, this does not necessarily correspond to chronological order. As noted in Chapter 3, research can make a contribution to the description of road safety problems by means of epidemiological studies of the contributions that various risk factors make to the current number of road accident fatalities and injuries (Stage 1 in Figure 6.1). Setting targets for improving road safety (Stage 2) is a profoundly political activity. Still, research can make a contribution by showing examples of targets that have been found to be more or less effective in the past. Research can also help prevent a self-contradictory formulation of a set of targets. Potentially effective road safety measures (Stage 3 in Figure 6.1) are described in this book. Making a survey of potentially effective road safety measure is therefore an activity that, to a large extent, has to be based on research. Stage 4 of the model sketched in Figure 6.1 again involves a more prominent political element. A framework for analysis of alternative policy options consists, among other things, of decisions
The Handbook of Road Safety Measures Copyright r 2009 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISBN: 978-1-84855-250-0
118
The Handbook of Road Safety Measures
Stage 1
Describe current road safety problems and assess their relative importance in contributing to fatalities and injuries
Stage 2
Develop road safety targets and decide on quantification of these as well as other policy objectives
Stage 3
Survey potentially effective road safety measures and decide which measures still have a potential for improving safety
Stage 4
Describe the current road transport system and establish a framework for analysis of alternative policy options
Stage 5
Develop alternative road safety policy options, showing the main directions for road safety policy
Stage 6
Estimate the effects of each policy option on the number of killed or injured road users, as well as effects with respect to other policy objectives
Stage 7
Assess sources of uncertainty in estimated effects and discuss the treatment of uncertainty in road safety policy making
Stage 8
Determine considerations relevant to the choice of road safety policy and choose preferred policy
Stage 9
Implement preferred road safety policy and evaluate effects of that policy
Figure 6.1: An analytical model of road safety policy-making.
made with respect to the constraints of road safety policy-making. For example, it is not uncommon to accept the following as constraints on road safety policy:
Current traffic volume and traffic growth is allowed to continue. No interventions are made with respect to the right of road users to choose mode of travel. Current budgets and their allocation between major items is continued.
Whether or not these constraints should be accepted as binding is, of course, entirely a political issue. Normative models of priority setting for public policy, such as cost– benefit analysis are intended to help policy-makers choose between alternative policy options. However, these models do not show how best to develop these options. Developing alternative policy options (Stage 5) is a political activity that involves judgements regarding both practical and political feasibility.
Part I: 6. The Contribution of Research to Road Safety Policy-Making
119
Estimating the effects of each policy option on road safety is an activity to which research can make a major contribution (Stage 6). The same applies to estimating uncertainty in the expected effects of a road safety policy option (Stage 7). The choice of a preferred policy is a political act (Stage 8). Research can inform this choice by analysing costs and benefits of alternative policy options. It is, however, a misunderstanding to think that the results of a cost–benefit analysis amount to a policy recommendation. A cost–benefit analysis sheds light on the efficiency in economic terms of alternative policy options. However, considerations other than efficiency in the strict sense of that term within the framework of cost–benefit analysis will nearly always be relevant for policy choice. It would therefore rarely, if ever, be the case that a cost–benefit analysis was the only basis for making a policy choice. Once road safety policy has been chosen, it is important to monitor and evaluate it (Stage 9). Unfortunately, this is not always done in a systematic way.
6.2 THE
APPLICABILITY OF COST–BENEFIT ANALYSIS
The use of cost–benefit analysis to inform the basis for making road safety policy is controversial. It is therefore perhaps instructive to discuss in more detail the applicability of cost–benefit analysis to road safety policy-making. To a large extent, the discussion is based on a paper by Elvik (2001b). Cost–benefit analysis has been applied for many years to set priorities for road safety measures. Its application goes at least 25 years back (Trilling 1978), but has remained controversial (Hauer 1994). In an early appraisal of the applicability of cost–benefit analysis to road safety measures, Joksch (1975) concluded that there were so many problems in estimating both costs and benefits and that one should not rely on cost– benefit analysis to decide whether road safety measures ought to be introduced. His objections did not, however, question the basic principles of cost–benefit analysis. Critics like Hauer (1991, 1994) and Haukeland (1994) have been more fundamental and reject the basic principles of cost–benefit analysis as put forward in the field of welfare economics to be applicable in the field of road safety. They state that the very idea of putting a monetary value on human life does not make sense and is ethically unacceptable. The implications for the applicability of cost–benefit analysis of various types of criticism against its use depend on the nature of the arguments made. If the basic principles of cost–benefit analysis are rejected, then the technique cannot be applied at all. If, on the other hand, the economic valuation of a certain non-marketed good is
120
The Handbook of Road Safety Measures
considered to be too uncertain, then more research is called for to obtain a more precise valuation. Most textbooks on cost–benefit analysis and applied economic welfare theory (Boadway and Bruce 1984, Dasgupta and Pearce 1972, Gramlich 1990, Hanley and Spash 1993, Johansson 1991, Layard and Glaister 1994, Mishan 1988, Sassone and Schaffer 1978, Sugden and Williams 1978, Williams and Giardina 1993) contain examples of such analyses, intended to show their basic logic. In general, the examples used to illustrate cost–benefit analysis in textbooks share the following characteristics:
They involve public expenditures, often investments. Projects are sometimes financed by direct user payment, but more often by general taxation. There are multiple policy objectives, often partly conflicting and requiring trade-offs to be made. It is assumed that policy-makers want solutions that realise all policy objectives to the maximum extent possible. One or more policy objectives concern the provision of a non-market public good, like less crime, a cleaner environment or safer roads. It is assumed that an efficient use of public funds is desirable, since such funds are scarce and alternative uses for them are numerous.
These are the main characteristics of problems that economists regard as well suited for cost–benefit analysis. The main principles of cost–benefit analysis. Applied welfare economics supplies the basic principles of cost–benefit analysis. There are four main principles:
Consumer sovereignty: The principle of consumer sovereignty, briefly stated, means that welfare is defined in terms of how consumers choose to spend their income between commodity bundles. The right of consumers to choose how to spend their income is respected. Valuation of goods according to willingness to pay: The strength of consumer preferences for the provision of public goods is measured by the amount of money that consumers are willing to pay for these goods. Various techniques have been developed to assess willingness to pay for non-marketed goods. It is beyond the scope of this book to discuss these techniques in detail. Welfare maximisation: The objective of cost–benefit analysis is welfare maximisation. Neutrality with respect to distribution of outcomes: Results from cost–benefit analyses are neutral with respect to the distribution of outcomes in the sense that they only show for which measures the benefits (in total) exceed the total costs. Cost–benefit analyses do not take into account possible differences between
Part I: 6. The Contribution of Research to Road Safety Policy-Making
121
different persons or groups and the costs may exceed benefits for some persons or groups. The applicability of cost–benefit analysis to road safety policy. The applicability of cost– benefit analysis to an area of public policy can be assessed by going through five stages. The stages are as follows:
Assess the basic principles of cost–benefit analysis Determine the type of issue to be decided Evaluate the suitability of policy objectives for cost–benefit analysis Determine if suitable policy programmes can be developed Evaluate the consequences of policy programmes, especially with respect to the possibility of monetary valuation.
Assess the basic principles of cost–benefit analysis. The first stage is to assess the basic principles of cost–benefit analysis. Those who reject these principles, rule out the use of cost–benefit analysis. A commonly held argument for rejecting the principle of consumer sovereignty is that road users are poorly informed about accident risks and have no idea of what it is like to be severely injured. Hence, it is argued that road users are not in a position to form well-informed preferences with respect to the need for improving road safety. Hauer (1994, 112) argues that trying to put a monetary value on human life is impossible, because it is ‘impossible to have preferences for an option involving the death of the deciding organism and it is meaningless to speak about them’. Against this, it can be argued that very many activities and choices that people are allowed to make influence their survival prospects. This is true of choice of occupation, where to live, how much and by what means to travel and lifestyle habits with respect to, for example, eating, exercising, sexual activity, smoking and alcohol consumption. All these choices can reasonably be modelled as lotteries involving death as one of their possible outcomes. It is far-fetched indeed to claim that people cannot intelligently make these choices, because there is a certain probability that death will be the outcome. There is always a certain probability that death may occur – in every human activity. There is nothing special about road traffic in this respect. Another common objection to using cost–benefit analysis to assess road safety measures is that the major policy objective ought to be to reduce the differences in accident rate between different groups of road users. Objections to cost–benefit analysis referring to how benefits and costs are distributed are based on the perception of the nature of the policy issue to be decided. Cost–benefit analysis is not equally well suited for all types of policy issues.
122
The Handbook of Road Safety Measures
Determine the type of issue to be decided. The second stage in an assessment of the applicability of cost–benefit analysis is, therefore, to determine the nature of the policy issue to be decided. Proponents of cost–benefit analysis recognise the fact that it is not appropriate to use the technique as an aid to help decide every type of issue. Some issues concern universal human rights, whose existence is not subject to a calculation of costs and benefits. Issues that concern the existence, exercise or protection of these rights are widely held to lie outside the scope of economic reasoning in terms of calculating costs and benefits. Issues that mainly concern justice and fairness are also widely held to lie outside the scope of cost–benefit analysis. It is important to note that the perception of a public policy issue is, at least to some extent, subjective. Whereas some people regard the provision of road safety mainly as a technical and economic issue, others regard it as a matter of bringing justice to those who are disproportionately at risk in the present road system. The former group may accept the use of cost–benefit analysis of road safety measures, whereas the latter group is likely to reject it. Evaluate the suitability of policy objectives for cost–benefit analysis. In order to allow for a cost–benefit analysis of policy options, policy objectives have to satisfy certain formal requirements. The first requirement is that policy objectives must be sufficiently clearly stated to make it possible to value their attainment in monetary terms. This does not necessarily require policy objectives to be quantified. On the contrary, quantified policy objectives may, depending on how they are formulated, be inconsistent with the principles of cost–benefit analysis. A policy objective must, however, be sufficiently clearly stated so that economists can design a study intended to assign monetary values to various levels of goal attainment. The second assumption made in cost–benefit analysis is that trade-offs between multiple policy objectives are legitimate. This means that a policy objective, which is lexicographically prior to all other objectives, is ruled out. An example of a target formulation fitting this description is Vision Zero for road accident fatalities. It states that there should not be any deaths or injuries resulting in permanent impairment in road traffic and explicitly rules out any trade-off of this objective against other policy objectives (Va¨gverket 1997). The third assumption, not explicitly stated in most textbooks, but recognised as important in practice by Eriksen, Killi and Minken (1994), is that policy objectives should not be highly controversial. Political controversies cannot be resolved by resorting to calculations of how much various policy objectives are ‘worth’ in monetary terms. If people disagree about the political objectives worth pursuing, this disagreement must be resolved either by majority vote or by negotiations that bring the different opinions closer together.
Part I: 6. The Contribution of Research to Road Safety Policy-Making
123
Determine if suitable policy programmes can be developed. The theory of cost–benefit analysis tells decision-makers to choose those policy programmes that give the greatest benefits in relation to costs. However, it does not tell decision-makers how best to develop alternative policy programmes to choose from. The policy options are simply taken as given, very little is said about how to obtain them. A policy programme will be judged acceptable according to a cost–benefit analysis only if its benefits are greater than costs. Purely symbolic programmes, designed merely to give an impression that something is being done to solve a problem, are not sanctioned by cost–benefit analysis. Cost–benefit analysis rests on an assumption that it is possible to separate means from ends. What this means can perhaps be clarified by means of an example. Suppose that an acceptably reliable estimate of the willingness to pay of the population for safer roads is available. A road safety programme is developed and a cost–benefit analysis is performed. Suppose it turns out that cost-effective road safety measures (measures for which benefits are greater than costs) can reduce the number of road accident fatalities by 25%. Assume further that a quantified target has been set for reducing the number of road accident fatalities by 50%. It is then against the rules of cost–benefit analysis to tamper with the willingness-to-pay estimate in order to design a programme, which reduces the number of fatalities by 50% cost-effective. A more appropriate conclusion is that the target is inconsistent with the willingness to pay for improved road safety. This example illustrates both what the principle of consumer sovereignty implies and how a quantified policy target can be inconsistent with the application of cost–benefit analysis. It also touches on a point that some safety advocates find particularly offensive, which is that one should limit the provision of road safety to what the population demands, as shown by willingness to pay. Evaluate the consequences of policy programmes, especially with respect to the possibility of monetary valuation. Cost–benefit analysis rests on the assumption that all economically relevant impacts of a project are valued in monetary terms according to the principles of welfare economics (Hanley and Spash 1993). An economically relevant impact is one that affects the utility of an individual. Roughly speaking, this means that all impacts that are subject to individual preferences are relevant. It is of course difficult to know when all economically relevant impacts have been included in a cost–benefit analysis. In recent years, the list of impacts that are included in a cost–benefit analysis has grown, as more and more items are valued in monetary terms. Despite this, there are still impacts that are not included in cost–benefit analyses. Road user security (feeling of safety) is a case in point.
124
The Handbook of Road Safety Measures
6.3 MONETARY
VALUATION OF ROAD SAFETY IN DIFFERENT COUNTRIES
A survey of official monetary valuations of road safety used in cost–benefit analyses in more than 20 motorised countries has been made at the Institute of Transport Economics (Elvik 2008b). The survey included valuations of traffic fatalities only. Figure 6.2 presents official valuations of the benefit to society of preventing a road accident fatality in 23 motorised countries. Figures are given in EUR at 2002 prices, adjusted to purchasing power parity. It is seen that the valuation of a traffic fatality varies enormously. Those factors that have been found to have the greatest influence on the valuation are real income per capita and the use of a willingness-to-pay method in order to obtain a monetary valuation of the benefits to society arising out of preventing traffic fatalities. Both these factors were associated with an increased valuation of road safety.
2,707,000
2,010,000
2,107,000
1,954,000
Switzerland
1,741,000
Sweden
1,704,131
1,273,372
Netherlands
1,266,000
Finland
1,408,630
1,193,378
Germany
New Zealand
1,170,695
Australia
899,014
791,748
221,392
679,737
206,087
Poland
589,177
181,920
Greece
462,717
150,253
Japan
55,812
1,000,000
316,334
2,000,000
Ireland
3,000,000
Spain
Value in EUR (2002-prices)
4,000,000
3,189,000
The large differences found in the monetary valuation assigned to traffic fatalities clearly shows that one should be very careful about applying the results of cost–benefit analyses of road safety measures made in one country to another. Although the costs of many road safety measures are likely to be somewhat lower in those countries that assign a low value to the prevention of a traffic fatality than in countries that assign a high value, it is unlikely that differences in cost fully compensate for the differences in the value given to the benefits of preventing road accident fatalities.
United States
Norway
Great Britain
Canada
Austria
Italy
Denmark
France
Belgium
Czech Republic
Portugal
-
Figure 6.2: Official monetary valuation of preventing a road accident fatality in 23 motorised countries – EUR 2002 prices at purchasing power parity.
Part I: 6. The Contribution of Research to Road Safety Policy-Making
125
Examples of cost–benefit analyses are given in many chapters of this book. These examples should be treated as numerical examples only, intended to illustrate the logic of cost–benefit analysis of the various road safety measures, and not as fully developed analyses whose results apply to other countries.
6.4 CURRENT
MONETARY VALUATIONS OF IMPACTS OF ROAD SAFETY
MEASURES IN
NORWAY
In several chapters in this book, examples are given of cost–benefit analyses that refer to Norwegian conditions. As an aid to understanding these examples, the monetary valuations of impacts of road safety measures that have been applied in the examples are presented below. Accident costs. The accident costs that are currently used in cost–benefit analyses were estimated in 1993 (Elvik 1993) and adjusted to 2005 prices in 2005 (Samstad, Killi and Hagman 2005). The accident costs are the sum of five main items:
Medical costs Loss of production capacity Costs of property damage Administrative costs Economic valuation of lost quality of life.
Given in 2005 prices, the unit costs per case of injury are stated in Table 6.1. The cost figures in Table 6.1 apply to each reported injured person and per injury accident reported to the police. In calculating the costs, account was taken of underreporting of accidents and injuries in official accident statistics. The cost figures therefore also include the costs of the unreported injuries. Vehicle operating costs, cost of travel time and environmental costs. Cost–benefit analyses include not only accident costs but also an economic evaluation of vehicle operating costs, cost of travel time and environmental costs. These costs will not be surveyed in detail in this book. Estimates for Norway stated in 2005 prices are presented in Table 6.2. These figures are social opportunity costs. This means that special taxes on petrol, for example, are not included in the operating costs for vehicles. Costs of travel time are stated per vehicle hour. This means that these costs cover both drivers and passengers
126
The Handbook of Road Safety Measures
Table 6.1: Costs of traffic accidents per injured person and per accident in Norway (NOK 2005 prices) Valuation unit
Total costs
Proportion loss of welfare (%)
Fatal injury
26,344,313
79
Very seriously injured
18,044,268
51
5,998,886
50
793,503
58
Injured person
Seriously injured Slightly injured Property damage only
49,374
0
2,269,420
58
Fatal accident
29,835,728
79
Injury accident
3,174,014
54
86,405
0
Average injuries Accident
Property damage only accidents
Table 6.2: Vehicle operating costs and costs of travel time (NOK 2005 prices) Type of vehicle Light vehicles Business travel
Operating costs, NOK per kilometre driven
Costs of travel time, NOK per vehicle hour
Costs of travel time, NOK per person hour
1.30
135
83.6
1.30
287
205.0
Trips to/from work
1.30
77
63.0
Private
1.30
115
57.0
Heavy vehicles
3.73
468
Bus
3.73
318
Bus (including person hours for passengers)
3.73
998
in vehicles. A valuation of travel time for bus passengers is included in travel time costs for buses. Environmental costs for road traffic have been estimated by Sælensminde and Hammer (1994). The costs include a monetary valuation of local air pollution, traffic noise and dust and dirt. Valuation is stated per person affected and varies depending on the size of the change in the environmental impacts. Calculated in 1995 NOK, the value of changes in environmental problems is given in Table 6.3.
Part I: 6. The Contribution of Research to Road Safety Policy-Making
127
Table 6.3: Valuation of the change in environmental problems stated per affected person (NOK 1995 prices, rounded to the nearest 50) Percentage change
Local air pollution
Traffic noise
Dust and dirt
þ20
5,150
2,550
1,750
þ10
2,550
1,300
900
10
1,650
550
500
20
3,300
1,150
1,050
30
5,050
1,700
1,550
40
6,100
2,050
1,850
50
7,150
2,350
2,200
60
8,200
2,650
2,500
70
9,300
2,950
2,800
80
10,400
3,250
3,100
90
11,450
3,600
3,450
In the cost–benefit analyses presented in this book, we have found it convenient to state environmental costs per kilometre driven, based on work done by Eriksen and Hovi (1995). Eriksen and Hovi have calculated environmental costs per kilometre driven for different types of vehicles. They also included the emission of carbon dioxide, in addition to the three environmental factors, which Sælensminde and Hammer have evaluated. Table 6.4 shows estimates of the environmental costs per kilometre driven for different types of vehicles, in 2005 prices, based on Samstad, Killi and Hagman (2005). The time horizon of cost–benefit analyses. In cost–benefit analyses, benefits and costs are estimated for the whole technical and economic lifetime of the measure. Future impacts are converted to present values using a discount rate. Table 6.5 shows the service life that is assumed for different main groups of road safety measures. The discount rate that has been used is 4.5% per year.
6.5 THE
PREVENTABILITY OF ROAD ACCIDENT FATALITIES AND INJURIES
Which risk factors can easily be controlled and how great a role do they play in the number of accidents? Fridstrøm (1999) has suggested that the factors that affect the number of accidents can be divided into the following groups with respect to how easily they can be influenced.
128
The Handbook of Road Safety Measures
Table 6.4: Environmental costs calculated in NOK per kilometre driven for different types of vehicle according to density of population (NOK 2005 prices, Samstad, Killi and Hagman 2005) Local emissions
Noise
Cities
Other densely built-up areas
Rural areas
Cities
Other densely built-up areas
Rural areas
Car (petrol)
0.064
0.041
0.016
0.311
0.310
0.000
Car (diesel)
0.600
0.191
0.009
0.309
0.309
0.000
Bus
4.575
2.038
0.178
2.906
2.925
0.000
Moped/mc
0.049
0.037
0.031
1.101
1.102
0.000
Passenger transport road
0.247
0.105
0.020
0.382
0.382
0.000
Vehicles
Truck (petrol) 3.5t þ
0.619
0.619
0.151
1.856
2.010
0.000
Truck (diesel) 3.5–7.5t
1.745
0.799
0.075
1.967
1.942
0.000
Truck (diesel) 7.5–16t
2.563
1.146
0.097
3.181
3.253
0.000
Truck (diesel) 16–23t
4.065
1.742
0.126
3.252
3.206
0.000
Truck (diesel) 23tþ
4.003
1.747
0.167
3.226
3.214
0.000
Trucks
3.136
1.397
0.123
2.818
2.832
0.000
Table 6.5: Service life for different groups of road safety measures Group of measures Road investment measures
Service life 25 years
Traffic signs, minor improvements to roads
10 years
Road markings
1–10 years (depending on amount of traffic)
Re-asphalting, new road surfaces
1–10 years (depending on amount of traffic)
Winter maintenance measures
1 year (1 winter)
Vehicle safety features for new vehicles
18 years
Vehicle safety features – retrofitted on the entire vehicle fleet
7.5 years
Vehicle inspections
1 year
Driver training measures
1–3 years
Traffic education for children
1–3 years
Information campaigns
1 year
Police enforcement
1 year
Sanctions (fines, imprisonment)
1 year
Withdrawal of driving licence
Period of withdrawal
Part I: 6. The Contribution of Research to Road Safety Policy-Making
129
First, accident numbers depend on a number of truly autonomous factors, determined outside the (national) social system, which can hardly be influenced by any (single) government, no matter how strong the political commitment is. Examples are weather, natural resources, the state of technology, the international price of oil, the size and structure of the population, etc. Second, they depend on a number of general socio-economic conditions, some of which are subject to political intervention. However, changes in such conditions are seldom an intended part of transport policy and they are rarely made with the primary purpose of promoting road safety. Examples are industrial development, unemployment, disposable income, consumption, taxation, inflation, public education, etc. At the third level, the size and structure of the transport sector, and the policy directed towards it have an influence on accident counts, although they usually are not intended as elements of road safety policy. Most importantly, many of these factors are strongly associated with exposure, that is, with the total volume of activities exposing the members of society to road accident risk. Examples are transport infrastructure, public transport, overall travel demand, modal choice, fuel and vehicle tax rates, size and structure of vehicle fleet, driving licence penetration rates, etc. Fourth, the accident statistics depend, of course, on the system of data collection. Accident under-reporting is the rule rather than the exception. Changes in the reporting routines are liable to produce fictitious changes in the accident counts. Fifth, accident counts, much like the throws of a die, are strongly influenced by sheer randomness, producing literally unexplainable variation. This source of variation is particularly prominent in small accident counts. For larger accident counts, the law of large numbers prevails, producing a relatively high
